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Record W6943800574 · doi:10.17632/n77bhwyp3y

Price, Investment and Leasing Data for the British Columbia ITQ Halibut Fishery

2019· dataset· en· W6943800574 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Investment (military)Economic dataFishingData sourceData fileData access

Abstract

fetched live from OpenAlex

The data that is presented here was aggregated and consolidated to inform the research presented in the dissertation (under review) Edwards, D., Addressing Questions on the Social and Economic Outcomes of an Individual Transferable Quota Fishery, A dissertation to be submitted in partial fulfillment of the requirements for the degree of doctor of philosophy, University of British Columbia and in the paper Edwards, D., & Pinkerton, E. (2019), The Hidden Role of Processors in an Individual Transferable Quota Fishery. Ecology and Society, In Press. This document describes the sources and methodology used for these data inputs. The source data is provided in accompanying files. The data sources presented here are all publicly available, compiled from multiple sources including published reports, online datasets, a government pay for access dataset (BC Online corporate registry), and government data requests (through the federal Access to Information process). A file providing additional background on the data presented, including data sources, is included.

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)0.005
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.049
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.017
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.015

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 head0.140
GPT teacher head0.321
Teacher spread0.181 · 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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Citations1
Published2019
Admission routes1
Has abstractyes

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