Ground-truthing of satellite imagery to track harmful algal blooms in Pigeon Lake, Alberta, Canada 2017-2022
Bibliographic record
Abstract
This data was collected to create a calibrated model that would enable the use of satellite imagery to track harmful algal blooms by using chlorophyll a estimates as a proxy for cyanobacteria in the lake. Samples from Pigeon Lake were collected on the same day that the Sentinel-2 satellite would pass over the lake. These samples were analyzed for different algal pigments and enumerated to genus level to ensure that the satellite imagery was of cyanobacteria rather than different algal groups. An algorithm was developed which we termed the three band index (TBI) that best matched with the cholorophyll a from the in situ samples. This model was used on satellite imagery from 2017-2022 of Pigeon Lake to get chlorophyll a estimates for every 20 x 20 pixel of each image of the lake. This pixel data was used to determine different bloom metrics like the intensity, the area (extent) and severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".