MétaCan
Menu
Back to cohort
Record W2025671518 · doi:10.1080/07055900.2011.592130

Mesoscale Temperature Patterns in the Rocky Mountains and Foothills Region of Southern Alberta

2011· article· en· W2025671518 on OpenAlexafffundvenueabout
Rachel M. Cullen, Shawn J. Marshall

Bibliographic record

VenueATMOSPHERE-OCEAN · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaParks Canada
KeywordsFoothillsLapse rateMesoscale meteorologyElevation (ballistics)Air temperatureGeologyClimatologyRange (aeronautics)Physical geographyEnvironmental scienceGeographyCartography

Abstract

fetched live from OpenAlex

Near-surface air temperatures have been monitored from 2005 to the present in a mesoscale network of 280 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations, topographic conditions and surface environments. In this paper, we report on the seasonal structure of temperature patterns and near-surface lapse rates in the region for the period 2005–09. Multivariate regression is used to model the influence of elevation, aspect, slope and surface cover on monthly mean temperatures. Regression models identify the influence of synoptic weather systems on regional temperature patterns and prevailing lapse rates. To explore this further, we examine daily temperature patterns under the influence of two common weather systems in the region: cold (continental polar) air masses and chinooks.

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 score0.034
Threshold uncertainty score0.069

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 studies0.0010.000
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 head0.018
GPT teacher head0.193
Teacher spread0.175 · 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

Citations37
Published2011
Admission routes4
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicCryospheric studies and observationsFrench-language works237,207