Satellite ocean colour based Harmful Algal Bloom identification for improved risk assessment and mitigation
Bibliographic record
Abstract
The aquaculture industry faces environmental threats from harmful algal blooms (HABs), which have the potential to cause devastating economic losses. Satellite earth observation offers a cost effective method for operational monitoring of HABs over vast areas. Whilst the Chl-a product, often used as a proxy for phytoplankton biomass, can be used to indicate high biomass blooms, there is a clear need for value-added products that not only alert on bloom presence, but also on the bloom type and persistence. The high biomass nature of the South African regional waters provide strong assemblage related spectral variability, which can be exploited with the spectral bands of OLCI and MERIS. This study demonstrates the identification of different phytoplankton types relevant to the aquaculture industry of South Africa. Thresholds of the reflectance peak in the red and NIR are related to phytoplankton count data, in order to identify blooms that pose a high hypoxia and/or toxicity risk. These techniques are applicable to both OLCI and MERIS reflectance data and are routinely used by the aquaculture industry in South Africa for timely risk assessment and mitigation. Application to both MERIS and OLCI not only ensures future monitoring capability, but also allows the creation of a historical risk climatology that can guide the site selection of industries sensitive to the presence of HABs, such as aquaculture farms and desalination plants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".