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Record W1602598426

Comprehensive Planning, Dominant-Use-Zones, and User Rights: a New Era in Ocean Governance

2010· article· en· W1602598426 on OpenAlexfundno aff
James N. Sanchirico, Josh Eagle, Stephen R. Palumbi, Barton H. Thompson

Bibliographic record

VenueScholar Commons (University of South Carolina) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersUniversity of California, DavisYork University
KeywordsZoningNegotiationVariety (cybernetics)Corporate governanceScope (computer science)Environmental resource managementBusinessEnvironmental planningEcosystem servicesFishingEcosystemGeographyPolitical scienceEcologyEconomicsComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

Ocean-zoning arguments often center on the biology of ocean species, the geography of fishing-use patterns, and the need for preventing use conflicts. Here we expand this discussion to the social and legal aspects of ocean zoning, focusing on comprehensive planning, segregation of activities into use-priority areas, and the allocation of user rights within each zone. The inclusion of all of these features within an ocean-zoning regime can be a catalyst for a variety of ancillary benefits, including opportunities for user groups to form informal or formal long-lived institutions and a reassessment of the focus and scope of the regulatory institutions involved in ocean management. Along with the ability of users to negotiate and trade within and between zones, both features will lead to improved conflict resolution, efficiency of use, and ecosystem stability—critical components for the production of ecosystem services and maintenance of biological and human economic benefits.

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)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.062
Scholarly communication0.0120.015
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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 designTheoretical or conceptual
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

Citations29
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueScholar Commons (University of South Carolina)Same topicCoastal and Marine ManagementFrench-language works237,207