Harmful algal blooms in the PICES region of the North Pacific
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ForewordBackground 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 TamaiRed tides and harmful algal blooms in Japan [pdf, 0.7 MB]People's Republic of China Tian Yan, Ming-Jiang Zhou and Jing-Zhong ZouA 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 LeeHarmful 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. ShevchenkoHarmful 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. HarrisonHarmful marine algal blooms in western Canada [pdf, 0.87 MB]United States of America Vera L. TrainerHarmful algal blooms on the U.S. west coast [pdf, 0.5 MB]Mexico Jose L. Ochoa, S. Lluch-Cota, B.O. Arredondo-Vega, E. Nuñes-Vázquez, A. Heredia-Tapia, J. Pérez-Linares and R. Alonso-RodriguezMarine Biotoxins and harmful algal blooms in Mexico's Pacific littora [pdf, 0.2 MB]Summary and conclusions [pdf, 0.6 MB] AppendicesA. 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it