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Record W6950419832 · doi:10.5683/sp2/fpyivc

Data from: How far have we come? A review of MPA network performance indicators in reaching qualitative elements of Aichi Target 11

2020· dataset· en· W6950419832 on OpenAlexaff

Bibliographic record

VenueBorealis · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of VictoriaMemorial University of Newfoundland
Fundersnot available
KeywordsScopusMarine protected areaKey (lock)Data collectionQualitative analysis

Abstract

fetched live from OpenAlex

This dataset is based on a systematic literature review to identify indicators used to assess MPA network effectiveness in achieving the qualitative elements. Web of Science core collection database (1900 to April 2019) and Elsevier’s Scopus database (1995 to April 2019) has been searched. Key search terms used in Web of Science and Scopus (last search date 08 April 2019 are "marine protected area network*" OR "marine reserve network*" OR "MPA network*" OR "no-take network*" OR "marine protected area system" OR "marine reserve system*” OR "MPA system*" OR "no-take system*" OR "LMMA network" OR "locally managed marine area network" OR "network of MPAs" OR "network of marine protected areas" OR “network of marine reserves” AND "effect*" OR "performance" OR “improve*” OR "success" OR “benefit" OR "enhance*" OR "impact*" OR "outcome" OR "support" OR "ecolog*" OR "abundance" OR "density” OR "size" OR "length" OR "biomass" OR “richness” OR "diversity" OR "habitat quality" OR "number" OR "social*" OR "livelihood" OR “health” OR "wellbeing" OR "well-being" OR "income" OR "employment" OR “ economic*” OR "support" OR "food security" OR “conflict” OR "participation" OR "biodiversity" OR "manage*" OR "equit*" OR “represent*“ OR "connect*" OR "integrate*" OR "governance" OR "adapt*"OR "touris*" OR "recreation". For all selected publications, we reviewed titles and abstracts to ensure that studies evaluated or discussed the effectiveness of some aspects of an MPA network or system of MPAs. Each of the final publications selected were coded for: (1) geographic location of the study; 2) one or more of the six Aichi Target 11 qualitative elements evaluated; (3) one or more of the dimensions covered by the research (ecological, social, economic, or governance); (4) the stages being evaluated in the process of effective management framework for the assessment of protected area management effectiveness; and (5) the variable(s) used to evaluate each element of the MPA network. Finally, (6) we hierarchically organized each variable into an indicator, noting that some variables were already indicators. See the related publication ( DOI: 10.1111/conl.12746 ) for more information.

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)0.027
metaresearch head score (Gemma)0.155
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: Dataset · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.155
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0270.040
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0410.003

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 head0.083
GPT teacher head0.364
Teacher spread0.281 · 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
GenreDataset

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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