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Record W3014167299 · doi:10.1038/s41586-020-2146-7

Rebuilding marine life

2020· review· en· W3014167299 on OpenAlexaff
Carlos M. Duarte, Susana Agustı́, Edward B. Barbier, Gregory L. Britten, Juan Carlos Castilla, Jean‐Pierre Gattuso, Robinson W. Fulweiler, Terry P. Hughes, Nancy­ Knowlton­, Catherine E. Lovelock, Heike K. Lotze, Milica Predragovic, Elvira S. Poloczanska, Callum M. Roberts, Boris Worm

Bibliographic record

VenueNature · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDalhousie University
FundersKing Abdullah University of Science and Technology
KeywordsMarine lifeSustainabilityMarine ecosystemMarine conservationClimate changeEnvironmental resource managementMarine protected areaHabitatEcosystemNatural resource economicsEnvironmental planningBusinessEcologyGeographyEnvironmental scienceBiologyEconomics

Abstract

fetched live from OpenAlex

Sustainable Development Goal 14 of the United Nations aims to “conserve and sustainably use the oceans, seas and marine resources for sustainable development”. Achieving this goal will require rebuilding the marine life-support systems that deliver the many benefits that society receives from a healthy ocean. Here we document the recovery of marine populations, habitats and ecosystems following past conservation interventions. Recovery rates across studies suggest that substantial recovery of the abundance, structure and function of marine life could be achieved by 2050, if major pressures—including climate change—are mitigated. Rebuilding marine life represents a doable Grand Challenge for humanity, an ethical obligation and a smart economic objective to achieve a sustainable future. Analyses of the recovery of marine populations, habitats and ecosystems following past conservation interventions indicate that substantial recovery of the abundance, structure and function of marine life could be achieved by 2050 if major pressures, including climate change, are mitigated.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.013
GPT teacher head0.271
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 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

Citations1,027
Published2020
Admission routes1
Has abstractyes

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