Harmful algal blooms in the PICES region of the North Pacific
Bibliographic record
Abstract
Foreword Background and objectives [pdf, 0.84 MB] Country reviews and status reports Section I. Western North Pacific Japan Yasuwo Fukuyo, Ichiro Imai, Masaaki Kodama and Kyoichi Tamai Red tides and harmful algal blooms in Japan [pdf, 0.7 MB] People's Republic of China Tian Yan, Ming-Jiang Zhou and Jing-Zhong Zou A national report of HABs in China [pdf, 0.24 MB] Republic of Korea Sam Geun Lee, Hak Gyoon Kim, Eon Seob Cho and Chang Kyu Lee Harmful algal blooms (red tides): Management and mitigation in Korea [pdf, 0.27 MB] Russia Tatiana Y. Orlova, Galina V. Konovalova, Inna V. Stonik, Tatiana V. Morozova and Olga G. Shevchenko Harmful algal blooms on the eastern coast of Russia [pdf, 1.4 MB] Section II. Eastern North Pacific Canada F.J.R. Max Taylor and Paul J. Harrison Harmful marine algal blooms in western Canada [pdf, 0.87 MB] United States of America Vera L. Trainer Harmful algal blooms on the U.S. west coast [pdf, 0.5 MB] Mexico Jose L. Ochoa, S. Lluch-Cota, B.O. Arredondo-Vega, E. Nunes-Vazquez, A. Heredia-Tapia, J. Perez-Linares and R. Alonso-Rodriguez Marine Biotoxins and harmful algal blooms in Mexico's Pacific littora [pdf, 0.2 MB] Summary and conclusions [pdf, 0.6 MB] Appendices A. Members of the Working Group [pdf, 0.1 MB] B. Original terms of reference (Vladivostok, 1999) [pdf, 0.08 MB] C. Annual reports of WG 15 [pdf, 0.15 MB] D. Workshop report on taxonomy and identification of HAB species and data management [pdf, 0.15 MB] (Document pdf contains 156 pages)
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".