6 Harmful marine algal blooms and climate change: progress on a formidable predictive challenge
Bibliographic record
Abstract
In a strict sense, harmful algal blooms are completely natural phenomena that haveoccurred throughout recorded history (e.g. Exodus, Captain Vancouver in 1793).Whereas in the past three decades unexpected new algal bloom phenomena haveoften been attributed to eutrophication or ship ballast water introduction,increasingly novel algal bloom episodes are now circumstantially linked to climatechange. It is unfortunate that so few long-term records exist of algal blooms at anysingle locality; ideally we need at least 30 consecutive years. Whether the apparentglobal increase in harmful algal blooms represents a real increase or not is thereforea question that we will probably not be able to answer conclusively for some time tocome. There is no doubt that our growing interest in using coastal waters for aquacultureis leading to a greater awareness of toxic algal species. People responsible fordeciding quotas for pollutant loadings of coastal waters, or for managing agricultureand deforestation, should be made aware that one probable outcome of allowingpolluting chemicals to seep into the environment will be an increase in harmful algalblooms. In countries that pride themselves on having disease and pollution-free aquaculture,every effort should be made to quarantine sensitive aquaculture areas againstthe unintentional introduction of non-indigenous harmful algal species. Nor can anyaquaculture industry afford not to monitor for an increasing number of harmful algalspecies in water and for an increasing number of algal toxins in seafood products, orto use increasingly sophisticated analytical techniques such as LC-MS. Last but notleast, global climate change is now adding a new level of uncertainty to many seafoodsafety monitoring programs.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".