Spatiotemporal Patterns of Chlorophyll-a Concentration in a Hypersaline Lake Using High Temporal Resolution Remotely Sensed Imagery
Bibliographic record
Abstract
The Great Salt Lake (GSL) is the largest saline lake in the Western Hemisphere. It supports billion-dollar industries and recreational activities, and is a vital stopping point for migratory birds. However, little is known about the spatiotemporal variation of phytoplankton biomass in the lake that supports these resources. Spectral reflectance provided by three remote sensing products was compared relative to their relationship with field measurements of chlorophyll a (Chl a). The MODIS product MCD43A4 with a 500 m spatial resolution provided the best overall ability to map the daily distribution of Chl a. The imagery indicated significant spatial variation in Chl a, with low concentrations in littoral areas and high concentrations in a nutrient-rich plume coming out of polluted embayment. Seasonal differences in Chl a showed higher concentrations in winter but lower in summer due to heavy brine shrimp (Artemia franciscana) grazing pressure. Twenty years of imagery revealed a 68% increase in Chl a, coinciding with a period of declining lake levels and increasing local human populations, with potentially major implications for the food web and biogeochemical cycling dynamics in the lake. The MCD43A4 daily cloud-free images produced by 16-day temporal composites of MODIS imagery provide a cost-effective and temporally dense means to monitor phytoplankton in the southern (47% surface area) portion of the GSL, but its remaining bays could not be effectively monitored due to shallow depths, and/or plankton with different pigments given extreme hypersaline conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".