Towards a ground-truthing of satellite imagery to track harmful algal blooms in Canadian prairie lakes
Bibliographic record
Abstract
A mounting task in the aquatic sciences is to better monitor harmful algal blooms (HABs). In Canada, satellite Earth Observation is used to meet this challenge in a few large lakes (e.g., Lake Erie, Lake Winnipeg). Here, we highlight the novel ground truthing of an algorithm for multivariate analyses of HABs in a smaller Canadian prairie lake (Pigeon Lake, Alberta) using Sentinel 2 satellite-based data and confirmatory evidence from in situ concentrations of chlorophyll a and taxonomically diagnostic algal pigments. Calibration ( r2 = 0.90, n = 73) and validation ( r2 = 0.91, n = 86) of our model enabled testing for trends and discovery of within-lake sources of HABs using archival satellite imagery spanning a period of 6 years. Significant seasonality of the intensity, spatial extent, and severity of HABs in the study lake underscored our discovery of their origins near inflowing streams within the northwestern littoral zone. These findings highlight the potential of our remote-sensing approach to identify “hotspots” of HABs and help guide remediation strategies in small prairie lakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".