Satellite-Based Time Series of Chlorophyll in Chilko Lake, British Columbia, Canada
Bibliographic record
Abstract
In Canada, many northern lakes are remote and difficult to access, with limited limnological data. Satellite sensors provide widespread coverage and growing time series of data unavailable via conventional sampling, but global validation is still limited. We evaluated chlorophyll estimates from the MERIS (Medium Resolution Imaging Spectrometer) sensor on board the European Space Agency (ESA) ENVISAT satellite for the ultra-oligotrophic Chilko Lake in the coastal mountains of central British Columbia. This lake supports a valuable sockeye salmon (Oncorhynchus nerka) population. We obtained good temporal coverage, through 1,425 scenes between June 18, 2002 and April 6, 2012. Although pre-processing was required to produce a high-quality dataset, one standard ESA algorithm generated chlorophyll estimates similar to field data. Regional and interannual phenological patterns were clear, and differences that may be important determinants of salmon production were well described. Although MERIS ceased operation in April 2012, it was replaced by the OLCI (Ocean and Land Color Instrument) on the SENTINEL 3a and 3b satellites launched in February 2016 and April 2018, respectively. We conclude that, with appropriate quality control and in situ validation, satellite-generated chlorophyll time series in sockeye salmon rearing lakes have significant potential as a fisheries planning and analysis tool.
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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) | 0.001 | 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 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".