The evolving and increasing need for climate change research on the oceans
Bibliographic record
Abstract
Abstract The 3rd International Symposium on the Effects of Climate Change on the World's Oceans was held in Santos, Brazil, in March 2015, convened by the International Council for the Exploration of the Sea (ICES), the North Pacific Marine Science Organization (PICES), and the Intergovernmental Oceanographic Commission of UNESCO (IOC), and organized locally by the Oceanographic Institute, University of Sao Paulo (IO-USP). The symposium was designed to do two things. First, to get updates on new scientific developments that would address recognized uncertainties that remained from Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report and to contribute to building bridges between research in the natural and social sciences in respect to the human dimensions of climate change, with a focus on coastal communities, management objectives, governance and adaptation measures. The choice of the venue in Santos, Brazil, was aimed to stimulate and widen this thematic discussion in Latin America and southern Atlantic regions, where there still are important knowledge gaps and scientific, politic and societal challenges to be overcome. The meeting was attended by 280 participants from 38 countries, contributing 336 oral and poster presentations. This paper summarizes the main outcomes of the symposium and introduces a number of papers submitted to this special issue.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".