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Record W2471964713 · doi:10.1093/icesjms/fsw052

The evolving and increasing need for climate change research on the oceans

2016· article· en· W2471964713 on OpenAlexaff
Manuel Barangé, Jacquelynne R. King, Luis Valdés, Alexander Turra

Bibliographic record

VenueICES Journal of Marine Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersAustralian Government
KeywordsClimate changeMarine researchPolitical scienceLatin AmericansGeographyCommissionCorporate governanceEnvironmental resource managementEnvironmental planningRegional scienceOceanographyEnvironmental scienceManagement

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.318
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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