University-National Oceanographic Laboratory System

Artificial intelligence (AI) is changing assessment in higher education in terms of automatic grading, adaptive testing, intelligent feedback, and distance proctoring. The paper considers the perceptions of AI-based assessment tools that hold popularity among the stakeholders of higher education (students, instructors, and administrators) in the context of perceived usefulness, fairness, reliability, ethical issues (privacy and bias), and impacts on academic integrity and learning outcomes. The study will gather quantitative survey data (N = 420) and qualitative interview data (n = 24) in a diverse sample of universities that have piloted or implemented AI assessment tools in 2020-2025 using a mixed-methods research design. Validated perceived usefulness, trust in automation, perceived fairness, privacy, and self-reported academic behavior are quantitative measures. Independent-samples t-tests to compare the attitudes of the students and instructors, multiple regression to predict the acceptance of the AI assessment based on the perceived usefulness and ethical issues, and ANOVA to determine variations across the disciplines and previous exposure to the AI tools are all tests of hypothesis. The thematic examination of human attitudes to pedagogy, academic integrity, transparency, and professional development requirements can be discussed as qualitative. The results show a conditional approval of the use of AI-based assessment: stakeholders appreciate efficiency and customized feedback, but they have constant reservations about the bias of algorithms, obscure grading, security threats to privacy through data transcriptions and remote proctoring, and the possible loss of human-driven assessment habits. The results of regression indicate that perceived usefulness ( =.42, p =.001), and perceived fairness ( =.29, p =.002) have positive prediction on acceptance, and privacy concern has negative prediction on acceptance ( = -.31, p =.001). T-tests are more persuasive of pragmatic acceptance among students than among instructors (t(418) = 3.12, p =.002). ANOVA shows that there is a difference in disciplinary difference: STEM participants indicated that they placed more trust in automated grading of objective tasks compared to humanities participants (F(3,416) = 6.21, p < .001). Qualitative topics talked of the necessity of human review, articulation of scoring reasons, strong data management, and employee education. The study has theoretical value, that is, it incorporates a technology acceptance and socio-technical fairness perspective and presents more practical recommendations: design transparent algorithms, embrace layered integrity, train educators, and engage stakeholders in assessment redesign. The authors conclude that AI-based testing can complement higher education assessment activities provided that the implementation is supported with safeguards against fairness, transparency, and pedagogic fit.

BackgroundGait impairments in Alzheimer’s disease (AD) and related dementias pose a major fall risk/contribute to morbidity/mortality. The Timed Up and Go (TUG) test is often used to assess mobility, gait changes, and dual‐task performance. The TUG‐Dual Task (TUG‐DT) version adds serial subtraction exercises to evaluate dual‐task cost (DTC). For cognitively unimpaired (CU) individuals or those with mild cognitive impairment (MCI), plasma biomarkers like pTau217, pTau181, and neurofilament light chain (NfL) can help assess the risk of AD. This study aimed to explore the relationship between TUG performance and plasma biomarkers in CU and MCI patients.MethodsParticipants included CU low‐risk (n = 75), CU high‐risk (n = 87), and CI (n = 32) older adults aged 55‐80, mean = .67.28 ± 6.062 years. Cognitive ability was assessed using the Clinical Dementia Rating Scale (CU = 0; CI = 0.5 or 1.0) and the Montreal Cognitive Assessment (CU ≥ 26; 18 ≤ CI ≤ 26). AD‐risk was determined by APOE genotyping and family history for CU groups. Plasma biomarkers pTau217, pTau181, and NfL were analyzed from fasting blood draws. Participants completed the TUG and TUG‐DT. ANOVAs, ANCOVAs, logistic regression, and generalized additive models (GAMs), were used to analyze the relationship between demographic factors, gait performance, and plasma biomarkers, with model comparisons guiding the final choice of GAMs for their flexibility in handling non‐linear relationships.ResultsStep count analysis on the TUG showed that the CU‐high‐risk and MCI groups performed similarly, while the CU‐low‐risk group completed significantly fewer steps than both. Plasma biomarkers, particularly pTau181 and NfL, interacted to predict gait performance only in the CU high‐risk group.ConclusionsThe TUG can predict plasma pTau217 levels with high specificity, distinguishing CU individuals not at risk for AD. Additionally, pTau181 and NfL interacted to predict performance on the TUG and TUG‐DT in the CU‐high‐risk group, suggesting subtle gait changes may signal early AD pathology. The CU‐low‐risk group’s reduced step count compared to others indicates preclinical AD might manifest with subtle mobility impairments. These findings support using simple gait tasks like the TUG for AD risk assessment in older adults.

ABSTRACT Controls of hydrology, landscape and soil characteristics on redox potentials, particularly at fine spatial and temporal scales, are poorly quantified and understood. Using high‐frequency sensors (30 min), the variability of redox potentials (Eh) in riparian soils was investigated for multiple depths and across diel, event and seasonal time scales over a period of 2 years. Sampling was performed for two riparian sites upstream of dams with contrasting soil textures. Incremental changes in soil moisture triggered abrupt and disproportionately large shifts in soil Eh values, a threshold‐like behaviour that has not been reported before. Contrary to expectations, Eh values initially decreased as the soil dried and then abruptly increased with further drying and oxidation of soil. Diel Eh patterns differed between sites, with the sand‐rich site oxidising by day and reducing at night, while the clay‐ and silt‐rich site showed the opposite pattern. These differences were attributed to differential oxygen diffusion and consumption by processes such as photosynthesis, evapotranspiration and microbial respiration. Sharp Eh shifts also occurred at soil depths with textural discontinuities. These redox hot spots and hot moments were attributed to differences in site topography, hydrology and soil characteristics such as texture, moisture, organic matter content and microbial biomass. Identifying and understanding these fine‐scale spatial and temporal patterns of Eh is critical for improved characterisation and quantification of biogeochemical processes in redox‐sensitive environments. This study underscores the value of high‐frequency redox sensors in making such assessments.

ABSTRACT With novel ocean conditions rapidly appearing as the result of climate change, basing decisions about fisheries and other ocean activities on historical conditions is no longer tenable. There is instead a widespread need for shifting ecological baselines to more effectively guide decisions into the future. What has not been as widely recognised is that the relevant timescales differ substantially across ocean‐related decisions, from lead times of hours to decades depending on the decision being made, and that this range necessitates a matching range of ecological forecast products across similar timescales. At the moment, a predictability gap exists at intermediate timescales, from multi‐annual to multi‐decadal forecasts. Because most fisheries and many other ocean activities rely on biological conditions like fish abundance or distribution, the ecological inertia of organismal growth, generational turnover, movement, and food web dynamics can help push ecological forecasts further across this gap. To realise this potential for more effective and usable ecological forecasts, coordinated research and implementation at the intersection of biology, climate science, social science, and decision‐making is needed. These efforts will be critical for forecasting shifting ecosystem baselines and sustaining fisheries, ocean ecosystems, and the ocean economy in the coming decades of rapid change.

Ecologists have long sought mechanisms to explain the productivity and diversity of coral reef communities while simultaneously seeking to predict the vulnerability and resilience of said communities to environmental change. We used compound-specific stable isotope analysis to examine how different sources of primary production support coral reef food webs. We found multiple lines of evidence for distinct end-member use among three Lutjanid snapper species that are typically considered "generalist" predators and thought to feed on multiple production sources on reefs. Instead, we found that Lutjanus kasmira foraged predominantly in a water column-based phytoplankton food web (74% of carbon contribution; 95% credible interval (Crl): 62%-85%), whereas L. ehrenbergii (58% [Crl: 42%-73%]) and L. fulviflamma (55% [Crl: 44%-67%]) partitioned resource use between benthic macroalgal and coral sources, respectively. These results indicate the existence of highly siloed carbon pathways in reef food webs across at least three trophic levels, with little mixing of primary producers among species. This siloing necessitates that the intermediary (primary and secondary) consumers in these food webs forage within the same tight energy silos, likely indicating the existence of strongly maintained microhabitats on the reefs that expose consumers within these compartments to prey items linked to different primary producers through isolated food web interactions. This work identifies important resource dimensions along which species separate, providing a compelling explanation for the remarkable diversity of coral reef fishes while simultaneously raising questions about the vulnerability of food web compartments to disturbances that threaten large-scale shifts in benthic community composition.

Mangrove vegetation can be easily recognized from remote-sensing satellite images compared to other terrestrial vegetation. The vegetation index is applied to the satellite images to highlight the aspect of vegetation density. This study aims to determine the correlation between the value of the vegetation index and mangrove canopy cover data to achieve a proper vegetation index to estimate the density of the mangrove canopy. The data needed are satellite imagery from Landsat 8 and mangrove canopy cover in sampling locations along the coast of Dodinga Bay, West Halmahera. Image data analysis includes radiometric correction, image sharpening, masking, classification, and accuracy tests. The vegetation index algorithms used were NDVI, GNDVI, and IM, and regression analysis was carried out for correlation tests. The analysis results obtained four different land cover classes with an overall accuracy of 97.70% and a kappa coefficient of 0.9688. The IM vegetation index showed an excellent correlation with mangrove canopy cover compared to GNDVI and NDVI. The determination coefficient (R²) of the IM is 0.6765; GNDVI (0.4897) and NDVI (0.4825). The IM classification produces four levels of mangrove canopy density, i.e., sparse (7.40 ha), moderate (628.33 ha), dense (921.22 ha), and very dense (16.45 ha). Keywords: mangrove, Landsat 8 images, vegetation index, Dodinga Bay Abstrak Objek vegetasi mangrove paling mudah diidentifikasi dengan menggunakan citra satelit penginderaan jauh dibandingkan objek vegetasi darat lainnya. Indeks vegetasi diterapkan terhadap citra untuk menonjolkan aspek kerapatan vegetasi. Penelitian ini bertujuan mengetahui korelasi antara nilai indeks vegetasi dengan data tutupan kanopi mangrove, sehingga didapatkan indeks vegetasi yang sesuai untuk menduga kerapatan kanopi mangrove. Data yang diperlukan yaitu citra Landsat 8 dan tutupan kanopi mangrove di lapangan. Analisis data citra terdiri dari koreksi radiometrik, penajaman citra, masking, klasifikasi dan uji akurasi. Algoritma indeks vegetasi yang digunakan yaitu NDVI, GNDVI dan IM, serta dilakukan analisis regresi untuk uji korelasi. Hasil analisis mendapatkan empat kelas tutupan lahan yang berbeda dengan overall akurasi sebesar 97,70 % dan kappa coefisien sebesar 0,9688. Indeks vegetasi IM menunjukkan korelasi sangat baik dengan tutupan kanopi mangrove dibandingkan GNDVI dan NDVI. Koefisien determinasi (R²) IM adalah 0,6765; GNDVI (0,4897) dan NDVI (0,4825). Klasifikasi IM menghasilkan empat tingkat kerapatan kanopi mangrove yaitu mangrove jarang (7,40 ha), mangrove sedang (628,33 ha), mangrove lebat (921,22 ha), dan mangrove sangat lebat (16,45 ha). Kata kunci: mangrove, citra Landsat 8, indeks vegetasi, Teluk Dodinga

Abstract Carbon flux to the deep sea can be dictated by surface ocean phytoplankton community composition, but translating surface ocean observations into quantitative predictions of carbon export requires additional consideration of the underlying ecosystem drivers. Here, we used genetic tracers of phytoplankton detected in surface seawater and within sinking particles collected in the mesopelagic ocean to identify mechanistic links between surface communities and carbon export in the North Pacific and North Atlantic Oceans. Phytoplankton 18S rRNA sequences were sampled over a one-month period in surface seawater and within bulk-collected and individually-isolated sinking particles using mesopelagic sediment traps (100-500m). Nearly all phytoplankton amplicon sequence variants (ASVs) exported from the surface were packaged in large (>300 µm) particles. Individually, these particles contained only a few distinct phytoplankton ASVs, but collectively, large particles transported about half of the surface taxonomic diversity into the mesopelagic. The relative sequence abundances of the surface community detected within particles were quantitatively related to measured POC fluxes: a linear model based on the relative sequence abundance of just two pigment-based phytoplankton taxa, diatoms and photosynthetic Hacrobia, was predictive of POC flux magnitude. These two taxa were also enriched within the ecologically-distinct particle classes that had the greatest influence on carbon export magnitude. As global, hyperspectral ocean color satellites begin to quantify these taxonomic groups in the surface ocean, the relationship of these taxa to carbon fluxes demonstrated here may help generate more accurate global estimates of export.

Marine animal forests (MAFs) are benthic ecosystems characterised by biogenic three-dimensional structures formed by suspension feeders such as corals, gorgonians, sponges and bivalves. They comprise highly diversified communities among the most productive in the world's oceans. However, MAFs are in decline due to global and local stressors that threaten the survival and growth of their foundational species and associated biodiversity. Innovative and scalable interventions are needed to address the degradation of MAFs and increase their resilience under global change. Surprisingly, few studies have considered trophic interactions and heterotrophic feeding of MAF suspension feeders as an integral component of MAF conservation. Yet, trophic interactions are important for nutrient cycling, energy flow within the food web, biodiversity, carbon sequestration, and MAF stability. This comprehensive review describes trophic interactions at all levels of ecological organisation in tropical, temperate, and cold-water MAFs. It examines the strengths and weaknesses of available tools for estimating the heterotrophic capacities of the foundational species in MAFs. It then discusses the threats that climate change poses to heterotrophic processes. Finally, it presents strategies for improving trophic interactions and heterotrophy, which can help to maintain the health and resilience of MAFs.