Bibliographic record
Abstract
CHOICE Highly Recommended, Sept 2022 Oceanography and Marine Biology: An Annual Review remains one of the most cited sources in marine science and oceanography. The ever-increasing interest in work in oceanography and marine biology and its relevance to global environmental issues, especially global climate change and its impacts, creates a demand for authoritative refereed reviews summarizing and synthesizing the results of recent research. For nearly 60 years, OMBAR has been an essential reference for research workers and students in all fields of marine science. This volume considers such diverse topics as the Great Barrier Reef Expedition of 1928-29, Mediterranean marine caves, macromedusae in eastern boundary currents, marine biodiversity in Korea, and development of a geo-ecological carbonate reef system model to predict responses of reefs to climate change. Volume 59 is available to read Open Access on the Taylor & Francis eBooks site (https://www.taylorfrancis.com/books//10.1201/9781003138846) An international Editorial Board ensures global relevance and expert peer review, with editors from Australia, Canada, Hong Kong, Ireland, Singapore and the United Kingdom. The series volumes find a place in the libraries of not only marine laboratories and oceanographic institutes, but also universities worldwide. If you are interested in submitting a review for consideration for publication in OMBAR, please email the Editor in Chief, Stephen Hawkins, at S.J.Hawkins@soton.ac.uk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.956 | 0.956 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".