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Record W4405388418 · doi:10.1007/s13280-024-02100-w

Pervasive Indigenous and local knowledge of tropical wild species

2024· article· en· W4405388418 on OpenAlexafffund
Yoshito Takasaki, Oliver T. Coomes, Christian Abizaid

Bibliographic record

VenueAMBIO · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of TorontoMcGill University
FundersJapan Society for the Promotion of ScienceSocial Sciences and Humanities Research Council of CanadaUniversity of TokyoUniversity of Toronto
KeywordsClearingHabitatIndigenousGeographyResource (disambiguation)Scale (ratio)SocioeconomicsEnvironmental resource managementEcologyBiologyBusinessEnvironmental scienceSociologyComputer scienceCartography

Abstract

fetched live from OpenAlex

The promise of Indigenous and local knowledge (ILK) for conservation policy depends on how pervasively ILK is held among local people. In the Peruvian Amazon, we conducted a landscape-scale concordance analysis between (1) ILK for game, timber, and fish species collected by the largest representative ILK survey as yet undertaken in tropical forests, and (2) remotely sensed land cover as proxies for species habitat. From our survey among 4000 households in 235 communities, we find that concordant ILK is highly pervasive across gender, age, place of origin, and social status, irrespective of species and people's indigeneity. Resource users possess more concordant knowledge than nonusers for timber and fish, not game. Concordance between ILK for fish and remote sensing is associated with cooperative forest clearing in shifting cultivation-an informal community institution in which forest peoples engage with nature. Our findings point to the promise of ILK for large-scale tropical conservation.

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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