Bibliographic record
Abstract
National Oceanic and Atmospheric Administration, Atlantic Oceanographic and Meteorological Laboratory, 4301 Rickenbacker Causeway, Miami, FL 33149, USA, Gustavo.Goni@noaa.gov, Molly.Baringer@noaa.gov, Silvia.Garzoli@noaa.gov University of California in San Diego, Scripps Institution of Oceanography, La Jolla, CA, droemmich@ucsd.edu University of Miami, Cooperative Institute for Marine and Atmospheric Studies, Miami, FL, Bob.Molinari@noaa.gov, Pedro.DiNezio@noaa.gov University of Tasmania, Hobart, Australia, Gary.Meyers@imos.org.au National Oceanographic and Meteorological Laboratory, National Oceanographic Data Center, Silver Spring, MD, Charles.Sun@noaa.gov, boyer@nodc.noaa.gov University of Hamburg, Hamburg, Germany, viktor.gouretski@zmaw.de ENEA, Centro Ricerche Ambiente Marino, Lerici, Italy, franco.reseghetti@santateresa.enea.it National Institute of Oceanography, Goa, India, vvgkxbt@yahoo.com University of Cape Town, Oceanography Department, Cape Town, South Africa, sswart@ocean.uct.ac.za Integrated Science Data Management, Ottawa, Canada, KeeleyR@DFO-MPO.GC.CA (11) University of Rhode Island, Graduate School of Oceanography, Narragansett, RI, trossby@gso.uri.edu Institut de Recherche pour le Developpement/Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales, Noumea, New Caledonia, christophe.maes@noumea.ird.nc LOCEAN, University of Paris VI, Paris, France, Gilles.Reverdin@lodyc.jussieu.fr
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".