Hyperspectral analysis of algal biomass in northern lakes, Churchill, MB, Canada
Bibliographic record
Abstract
A hyperspectral approach to quantify algal biomass was studied across 30 shallow ponds in the Hudson Bay Lowlands near Churchill, MB. Normalized difference algal indices (NDAI) were calculated based on hyperspectral measurements of the reflectance collected on shore with a hand-held spectrometer in parallel to estimations of biomass with an in vivo fluorometer designed for benthic algae. Algal biomass and coarse assemblages were differentiated through their spectral reflectance as a demonstration of concept for future upscaling that would be necessary for regional monitoring using remote sensing technology. Results indicated strong agreements between the calculated NDAI for measured reflectance from each pond and that of the isolated benthic zone. Cyanobacteria were the dominant component of the algal community for most ponds. As such, measures of reflectance and use of simple NDAIs may be able to characterize the total biomass of northern ponds. However, the distinction between algal groups may require independent validation of algal assemblages for estimations beyond total biomass. Nonetheless, hyperspectral analysis could provide a strong potential for monitoring northern freshwater systems at a regional scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".