MétaCan
Menu
Back to cohort
Record W2125592715 · doi:10.1088/1748-9326/7/1/014028

Observed decreases in the Canadian outdoor skating season due to recent winter warming

2012· article· en· W2125592715 on OpenAlexaffabout
Nikolay Nikolaev Damyanov, H. Damon Matthews, Lawrence A. Mysak

Bibliographic record

VenueEnvironmental Research Letters · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsGlobal warmingEnvironmental scienceClimate changeWinter seasonProxy (statistics)ClimatologyRecreationPhysical geographyGeographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Global warming has the potential to negatively affect one of Canada's primary sources of winter recreation: hockey and ice skating on outdoor rinks. Observed changes in winter temperatures in Canada suggest changes in the meteorological conditions required to support the creation and maintenance of outdoor skating rinks; while there have been observed increases in the ice-free period of several natural water bodies, there has been no study of potential trends in the duration of the season supporting the construction of outdoor skating rinks. Here we show that the outdoor skating season (OSS) in Canada has significantly shortened in many regions of the country as a result of changing climate conditions. We first established a meteorological criterion for the beginning, and a proxy for the length of the OSS. We extracted this information from daily maximum temperature observations from 1951 to 2005, and tested it for significant changes over time due to global warming as well as due to changes in patterns of large-scale natural climate variability. We found that many locations have seen a statistically significant decrease in the OSS length, particularly in Southwest and Central Canada. This suggests that future global warming has the potential to significantly compromise the viability of outdoor skating in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.096
GPT teacher head0.285
Teacher spread0.189 · 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 teacher head, not a consensus.

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

Citations30
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Research LettersSame topicCryospheric studies and observationsFrench-language works237,207