MétaCan
Menu
Back to cohort
Record W2167499373 · doi:10.1080/19338244.2015.1058235

Mapping rural community and dairy cow heat stress in Southern Ontario: A common geographic pattern from 2010 to 2012

2015· article· en· W2167499373 on OpenAlexaffabout
Katherine E. Bishop-Williams, Olaf Berke, David L. Pearl, D.F. Kelton

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHeat stressHeat waveHeat illnessClimate changeUrban heat islandAgricultureEnvironmental scienceExtreme heatGeographyEnvironmental healthClimatologyMeteorologyMedicineAtmospheric sciencesGeologyOceanography

Abstract

fetched live from OpenAlex

Climate change has increased the occurrence of heat waves, causing heat stress among humans and livestock, with potentially fatal consequences. Heat stress maps provide information about related health risks and insight for control strategies. Weather data were collected throughout Southern Ontario, and the heat stress index (HSI) was estimated for 2010-2012. Geostatistical kriging was applied to map heat stress, heat waves, and control periods. Average HSI for each period ranged from 55 to 78 during control periods, and from 65 to 84 during heat waves, surpassing levels where morbidity is known to increase substantially. Heat stress followed a temporally consistent geographic pattern. HSI maps indicate high-risk areas for heat-related illness and indicate areas where agriculture and human health may be at increased risk in future.

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 score0.017
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.055
GPT teacher head0.299
Teacher spread0.244 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueArchives of Environmental & Occupational HealthSame topicClimate Change and Health ImpactsFrench-language works237,207