MétaCan
Menu
Back to cohort
Record W2914093658 · doi:10.1126/science.aav1004

Ocean recoveries for tomorrow’s Earth: Hitting a moving target

2019· review· en· W2914093658 on OpenAlexfundno aff
Kurt E. Ingeman, Jameal F. Samhouri, Adrian C. Stier

Bibliographic record

VenueScience · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersSociety for Conservation BiologyMcGill UniversityCedar Tree Foundation
KeywordsMarine ecosystemEcosystemPopulationEarth system scienceEnvironmental resource managementEnvironmental scienceDual (grammatical number)Sustainable developmentOceanographyNatural resource economicsEarth scienceGeologyEcologyBiologySociologyEconomics

Abstract

fetched live from OpenAlex

Growing scientific awareness, strong regulations, and effective management have begun to fulfill the promise of recovery in the ocean. However, many efforts toward ocean recovery remain unsuccessful, in part because marine ecosystems and the human societies that depend upon them are constantly changing. Furthermore, recovery efforts are embedded in marine social-ecological systems where large-scale dynamics can inhibit recovery. We argue that the ways forward are to (i) rethink an inclusive definition of recovery that embraces a diversity of stakeholder perspectives about acceptable recovery goals and ecosystem outcomes; (ii) encourage research that enables anticipation of feasible recovery states and identifies pathways toward resilient ecosystems; and (iii) adopt policies that are sufficiently nimble to keep pace with rapid change and governance that works seamlessly from local to regional scales. Application of these principles can facilitate successful recoveries in a world where environmental conditions and social imperatives are constantly shifting.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.062
GPT teacher head0.333
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations60
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicMarine and fisheries researchFrench-language works237,207