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Record W7161980236 · doi:10.82308/18188

Impacts of climate change on traditional food security in aboriginal communities in Northern Canada

2007· dissertation· en· W7161980236 on OpenAlexaboutno aff
Melissa Guyot

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityFood consumptionFood supplyConsumption (sociology)Climate change

Abstract

fetched live from OpenAlex

Cette thèse regard l'impact des changements environmentales sur la récolte des aliments traditionnels et characterise l'implication de ses changements sur la diète des membres de la communauté. Une combinaison de méthode quantitative et qualitative ont été utilisé pour documenter et estimer la séquence de la récolte des animaux clées locales. En général, les résultats entre la nourriture disponible estimé provennant de la récolte et le montant estimé pour la consumption alimentaire n'étaient pas égaux, parcontre, la ratio entre aurignal et poisson blanc étaient bonne. La relation entre les résultats numériques concernent la récolte et la consumption alimentaire sont complèxes et requièrent deux coordonnées d'informations numériques completes. Si cela existe, il serait possible de prédir la consumption des aliments traditionalles provenant de la récolte. Les résultats qualitatifs dénoncent des changements climatiques affectant la récolte des aliments traditionnels et alterent la façon dont les membres de la communauté font leur récolte pour adapter à ces changements climatiques.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.393
Teacher spread0.330 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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