ChloroGIN: Use of Satellite and In Situ Data in Support of Ecosystem-Based Management of Marine Resources
Bibliographic record
Abstract
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK, ssat@pml.ac.uk; UNESCO, Intergovernmental Oceanographic Commission, 1 rue Miollis, 75732 Paris cedex 15, France, J.Ahanhanzo@unesco.org; Council for Scientific and Industrial Research, Meiring Naude Rd, Pretoria, Gauteng 0184, South Africa, sbernard@csir.co.za; National Oceanography Centre Southampton, University of Southampton Waterfront Campus, European Way, Southampton SO14 3ZH, UK, val.byfield@noc.soton.ac.uk; Dalhousie University, Department of Oceanography, 1355 Oxford Street, Halifax, NS Canada, B3H 4J1, lisa.delaney@dal.ca; European Commission, Joint Research Centre, Via E. Fermi, 2749 I-21027 Ispra (VA), Italy, mark.dowell@jrc.ec.europa.eu; Marine Research (MA-RE) Institute, University of Cape Town, P Bag X 3, Rondebosch 7701, South Africa, jgfielduct@gmail.com; Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK, sbg@pml.ac.uk; Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK, nhmo@pml.ac.uk; European Commission, Joint Research Centre, Via E. Fermi, 2749 I-21027 Ispra (VA), Italy, nicolas.hoepffner@jrc.ec.europa.eu; Vision on Technology (Vito), Boeretang 200, B-2400 Mol, Belgium, tim.jacobs@vito.be; National Institute for Space Research (INPE), Av dos Astronautas, 1.758, Jd. Granja CEP: 12227-010, Sao Jose dos Campos – SP, Brazil, milton@dsr.inpe.br; Indian National Centre for Information Services, Ocean Valley, P.B No.21, IDA Jeedimetla P.O, Hyderabad 500 055, India, srinivas@incois.gov.in; Instituto Nacional de Investifacion y Desarrollo Pesquero, Paseo Victoria Ocampo No1, Escollera Norte, B7602HSA Mar del Plata, Buenos Aires, Argentina, vlutz@inidep.edu.ar; Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK, tplatt@dal.ca.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".