Bibliographic record
Abstract
Introduction As White and Frady (1995) say in the Preface of their recent international directory on experts in toxic and harmful algae, ‘Toxic and harmful algal blooms present a growing global problem for fisheries, aquaculture, and public health.’ With entries from 58 countries, they list 22 countries with names and addresses of people working with harmful diatoms and/or their toxins: Australia (3), Canada (29), Chile (1), Croatia (1), Denmark (4), Germany (3), India (3), Israel (1), Japan (6), Netherlands (3), New Zealand (3), Norway (4), People's Republic of China (17), Republic of Korea (2), Romania (1), Russian Federation (1), Spain (6), Thailand (1), Turkey (1), United Kingdom (2), United States of America (29), and Vietnam (1), for a total of 122 workers around the world. One such list of international specialists was compiled by Woods Hole Oceanographic Institution Sea Grant Program in 1990 (subsequently updated), so that the consequences of outbreaks of toxic and harmful algal bloom events on fisheries and public health could be reduced. The purpose of this summary chapter is to serve as a resource for those faced with the challenges brought about by the changing dominant coastal diatom flora. In the study of toxic and harmful diatoms, there are applications for fisheries, public health institutions, mariculture, and tourism. Harmful blooms ‘A first step in applied ecology is to accurately define the present state of the environment’, according to Rowe (1996, p. 7), and progress in this endeavor has been made.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".