Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)
Bibliographic record
Abstract
A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".