Lake of the Woods Satellite-derived Annual Algal Bloom Indices
Bibliographic record
Abstract
This dataset contains annual algal bloom indices (duration, extent, intensity, severity) summary statistics (maximum, mean) over the June through October monitoring period for Lake of the Woods. These data are derived from satellite remote sensing algorithms using European Space Agency's (ESA's) MEdium Resolution Imaging Spectrometer (MERIS) sensor on the Envisat satellite (2002-2012) and ESA's Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel 3A and Sentinel 3B satellites (2016-present). At the end of each monitoring season, data are presented in annual report format including current and historical summary statistics of algal bloom indices, alongside relevant imagery. Citation: Binding, C.E., Pizzolato, L., & Zeng, C. (2021). EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes. Ecological Indicators, 121, 106999. doi:10.1016/j.ecolind.2020.106999
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".