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Record W4224129144 · doi:10.22215/etd/2022-14858

Evaluating Evidence-informed Decision-making in the Management and Conservation of British Columbia’s Fish and Wildlife Resources

2022· dissertation· en· W4224129144 on OpenAlexafffundabout
Andrew N. Kadykalo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaGenome British ColumbiaFreshwater Fisheries Society of British ColumbiaGenome Canada
KeywordsWildlifeStakeholderIndigenousEmpirical evidenceEnvironmental resource managementWildlife conservationGeographyNatural resourceWildlife managementNatural resource managementPublic relationsEnvironmental planningFisheryPolitical scienceEcologyBiology

Abstract

fetched live from OpenAlex

Wildlife managers are faced with decisions and issues that are increasingly complex, spanning natural and human dimensions. A strong evidence base that includes multiple forms and sources of knowledge would support these complex decisions. However, a growing body of literature demonstrates that environmental managers are far more likely to draw on intuition, experience, or opinion to inform important decisions rather than empirical evidence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.356
Teacher spread0.308 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2022
Admission routes3
Has abstractyes

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