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Record W2984992821 · doi:10.1088/1748-9326/ab54f3

Defining frigid winter illuminates its loss across seasonally snow-covered areas of eastern North America

2019· article· en· W2984992821 on OpenAlexaff
Alexandra R. Contosta, Nora J. Casson, Sarah J. Nelson, Sarah Garlick

Bibliographic record

VenueEnvironmental Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Winnipeg
FundersLintilhac FoundationNortheastern States Research CooperativeDavis Conservation Foundation
KeywordsSnowSnow coverPeriod (music)EcosystemClimatologyPhysical geographyCold winterEnvironmental scienceGeographyEcologyMeteorologyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Winter is often understudied in ecosystem sciences and viewed as a burden for human systems and infrastructure. However, the importance of winter in regulating ecological processes and shaping human communities has emerged as a topic of great interest, particularly in areas that experience seasonal snow cover. Traditional seasonal definitions may not fully represent below freezing winters and snow accumulation that have historically characterized these areas. Here we: (1) propose the concept of ‘frigid winter’ to address longstanding problems with traditional delineations of winter; and (2) define frigid winter as a period of sustained temperatures below freezing and snow accumulation that together regulate ecological processes and their services. We explore this definition and the changes occurring within it using 100 years of meteorological data from northeastern North America. Trend analysis demonstrates that frigid winters have shortened by ∼3 weeks over the last century, that cold, snowy conditions have become more intermittent, and that the choice of winter delineation (astronomical, meteorological, hibernal, or frigid) influences the apparent rate at which winter conditions disappear.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2019
Admission routes1
Has abstractyes

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