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Record W4235826520 · doi:10.22215/etd/2014-10263

The Climatic Implications of Lake Level Expansion in the Mackenzie Bison Sanctuary, Fort Providence, Northwest Territories

2014· dissertation· en· W4235826520 on OpenAlexfundaboutno aff
Peter deMontigny

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAboriginal Affairs and Northern Development CanadaAurora Research Institute
KeywordsClimate changeGeographyDendrochronologyPhysical geographyGrasslandEcologyArchaeologyBiology

Abstract

fetched live from OpenAlex

Remotely sensed data indicates that lake expansion north of Fort Providence, Northwest Territories, is statistically significant, potentially contributing to wood bison (Bison bison athabascae) migrating beyond the Mackenzie Bison Sanctuary.The Mackenzie herd is one of the few remaining populations not infected by bovine brucellosis (Brucella abortus) and tuberculosis (Mycobacterium bovis).Interaction with nearby infected herds could introduce widespread infection.Lake expansion is often driven by changes in climate, however climate records for this region are lacking.Dendrochronology can be used to examine longer-term climate.Climate was reconstructed using nine white spruce (Picea glauca) chronologies.Correlations were highest between the chronologies and the Palmer Drought Severity Index, which show climate variability has increased within the study area since 1915.Remote sensing results correlate with positive phases of the July-October Pacific North American pattern, however the freezing date of the active layer may provide a better understanding of water level fluctuations.

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)0.000
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 score0.466
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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
Published2014
Admission routes2
Has abstractyes

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