Bibliographic record
Abstract
Harmful Algal Blooms in a Changing Climate,<em> Raphael Ku</em>......................................................1 New Initiative on Fish-Killing Algal Blooms, <em>Leonardo Guzmán & Gustaaf Hallegraeff</em>.......................2 IOC-SCOR GlobalHAB Workshop: Evaluating, Reducing and Mitigating the Cost of Harmful Algal Blooms: a Compendium of Case Studies, <em>Vera L Trainer, Keith Davidson, Kazumi Wakita, Elisa Berdalet, Marc Suddleso, Geir Myre & Dean Trethewey</em>..............................................................................3 Massive fish mortality in Teluk Bahang, Penang, Malaysia caused by a hypoxia-inducing algal bloom, <em>Kieng Soon Hii, Zhen Fei Lim, Li Keat Lee, Suh,Nih Tan, Aini Hannani Naqiah Abdul Manaff, Po Teen Lim & Chui Pin Leaw,</em>……………………………………………………………………………………………….………5 Blooms of the potentially harmful raphidophyte <em>Chattonella antiqua </em>and the occurrence of the epiphytic dinoflagellat<em>e Ostreopsis </em>cf.<em> ovata </em>in the coastal waters of Alexandria, Egypt, <em>Shimaa Hosny & Wagdy Labib</em>……………………………………………………………………………………….…………6 First records of <em>Gambierdiscus</em> <em>excentricus</em> and <em>Ostreopsis lenticularis</em> in the Cape Verde Archipelago (Macaronesia, Central Eastern Atlanctic), <em>Emilio Soler Onís, Juan Fernández Zabala, Ana S Ramirez</em>………………….8 <em>Microcystis</em> bloom in Saladito River, central-southern Cuba, <em>Aimee Valle-Pombrol, Augusto Comas-</em><em>González, Minerva Sánchez-Llull & Angel R. Moreira-González,</em>.................................................................10 Citizen Science Oceanography in the Strait of Georgia, Canada-– an overview of five years of operations, <em>Svetlana Esenkulova & Isobel Pearsall…………..</em>...................................................................12 Multicoloured algal blooms in the NW Adriatic during 2018, <em>Cecilia Totti, Tiziana Romagnoli & Stefano Accoroni</em> .............................................14 A bloom of <em>Prorocentrum triestinum </em>in the Hossegor Marine Lake (France), <em>Myriam Perrière-Rumèbe & Claire Meteigner</em>.................................................................................................................16 The ICES Annual Science Conference 2019, <em>Eileen Bresnan, Sophie Pitois & Mike Rust</em>..........…………17 In memoriam, Professor Tufan Koray, <em>Sibel Bargu, Nihayet Bizsel & Kemal Can Bizsel,</em>.............................17 11<sup>th</sup> Irish Shellfish Safety Workshop, <em>Dave Clarke..</em>.......................................................................................18 The 33rd annual meeting of the Australasian Society for Phycology and Aquatic Botany (ASPAB), <em>Tomohiro Nishimura, Lesley Rhodes, Arjun Verma & Judy Sutherland……</em>..........................................20 The 19<sup>th </sup>ICHA in México.....................................................................................................................................21 Modelling and Prediction of Harmful Algal Blooms: Emerging Methods, Regional Problem-solving, and Patterns of Global Change...........................................................................................................21 DINO 12 in the Canary Islands........................................................................................................................ 22
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.170 | 0.099 |
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".