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Record W4214941914 · doi:10.1016/j.marpol.2022.105006

Assessment of scientific gaps related to the effective environmental management of deep-seabed mining

2022· article· en· W4214941914 on OpenAlexaff
Diva J. Amon, Sabine Gollner, Telmo Morato, Craig R. Smith, Chong Chen, Sabine Christiansen, Bronwen Currie, Jeffrey C. Drazen, Tomohiko Fukushima, Matthew Gianni, Kristina M. Gjerde, Andrew J. Gooday, Georgina Guillen Grillo, Matthias Haeckel, Thembile Joyini, Se‐Jong Ju, Lisa A. Levin, Anna Meta×as, Kamila Mianowicz, Tina N. Molodtsova, Ingo Narberhaus, Beth N. Orcutt, Alison Swaddling, Joshua Tuhumwire, Patricio Urueña Palacio, Michelle Walker, P.P.E. Weaver, Xue‐Wei Xu, Clement Yow Mulalap, Peter Edwards, Chris Pickens

Bibliographic record

VenueMarine Policy · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsDalhousie University
FundersNational Oceanic and Atmospheric AdministrationNederlandse Organisatie voor Wetenschappelijk OnderzoekSight Research UKBundesministerium für Bildung und ForschungNatural Environment Research CouncilGordon and Betty Moore FoundationPew Charitable Trusts
KeywordsHarmObligationUnited Nations Convention on the Law of the SeaEnvironmental resource managementSeabedEnvironmental planningBusinessTask (project management)Deep seaConventionComputer scienceEnvironmental sciencePolitical scienceOceanographyLawEngineeringGeology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.166
metaresearch head score (Gemma)0.250
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1660.250
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0170.014
Science and technology studies0.0040.013
Scholarly communication0.0140.028
Open science0.0050.013
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0090.001

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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.

Study designSystematic review
DomainEvaluation
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

Citations190
Published2022
Admission routes1
Has abstractyes

Explore more

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