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Record W6991104937

Exploration of the Spatio-Temporal Variability of Ground Temperature Across a Subalpine-Alpine Ecotone

2022· dissertation· en· W6991104937 on OpenAlexaffabout

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEcotoneVegetation (pathology)Vegetation coverClimate changeEcosystemAir temperaturePrecipitationSpatial variability
DOInot available

Abstract

fetched live from OpenAlex

With the very dynamic and unpredictable climate experienced in recent years, it is becoming increasingly apparent that an in depth understanding of the processes that occur within mountain ecosystems is vital. The purpose of this study was to investigate the spatio-temporal patterns of ground surface temperature across a subalpine-alpine ecotone on Vancouver Island, and to identify the biotic site characteristics contributing to these patterns. The spatial and temporal patterns were identified using an empirical orthogonal function (EOF) analysis, and relationships between these patterns and site characteristics were also explored. A few significant relationships were found, indicating that vegetation cover impacts ground temperature measurements at these locations on Vancouver Island. There were also significant relationships found between mean temperatures at both sites, with the alpine site generally experiencing hotter temperatures in summer and colder in winter. Lastly, scarification, the breaking up of surface topsoil, increased temperatures within plots at both sites.

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 score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.240
Teacher spread0.212 · 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
Published2022
Admission routes2
Has abstractyes

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