An assessment of MERIS algal products during an intense bloom in Lake of the Woods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lake of the Woods (LoW) is an international (USA/Canada) inland water body under significant water quality pressures from recurring cyanobacteria blooms. Its remote location combined with the hydrologically complex nature of its waters makes adequate in situ monitoring of the lake difficult. This work aimed to test the potential of Envisat's Medium Resolution Imaging Spectrometer (MERIS) full-resolution imagery for monitoring algal blooms in the lake. A full assessment of MERIS L1 and L2 chlorophyll and chlorophyll-related products was carried out over LoW during an intense surface algal bloom in September 2009. The Case 2 regional model and fluorescence line height/maximum chlorophyll index (MCI) plug-ins for BEAM were assessed for their ability to accurately distinguish the bloom. Results suggest that none of the Case-2-specific algorithms effectively extract chlorophyll concentrations over LoW, whereas the greatest potential is seen within the MCI product. Adjacency effects in near-shore waters are shown to be significant, although the improved contrast between ocean and land processor (ICOL) does not appear to notably improve water constituent retrievals in these waters. Images of L2 MCI are shown to adequately identify the bloom and are used to track the evolution of the bloom across the lake. Evidence is presented for the effects of variable depth distributions of cyanobacteria on the surface signal seen by the sensor; imagery suggests that day-to-day variations in wind-induced mixing have a profound impact on surface algal biomass as detected by remote sensing.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it