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Record W2956318100 · doi:10.1002/lno.11239

Reaching a breaking point: How is climate change influencing the timing of ice breakup in lakes across the northern hemisphere?

2019· article· en· W2956318100 on OpenAlexafffund
Lianna Lopez, Bailey Hewitt, Sapna Sharma

Bibliographic record

VenueLimnology and Oceanography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBreakupNorthern HemisphereClimatologyPrecipitationEnvironmental scienceCryosphereClimate changeLatitudeSouthern HemisphereSpring (device)Atmospheric sciencesSea icePhysical geographyGeologyGeographyOceanographyMeteorology

Abstract

fetched live from OpenAlex

Abstract The duration of seasonal winter ice cover has declined in many mid‐ and high‐latitude regions around the world as climate continues to warm. We obtained data on lake ice breakup dates, air temperature, precipitation, and large‐scale climate oscillations for 152 lakes across the northern hemisphere from 1951 to 2014. Ninety‐seven percent of study lakes exhibited earlier ice breakup trends. Forty‐six percent of the variation in ice breakup trends was driven by spring air temperatures and elevation across the northern hemisphere. However, changes in ice breakup have not always been in a gradual or linear pattern. Using the sequential T ‐test analysis of regime shifts, we found evidence of abrupt changes in mean ice breakup for 53% of lakes with shift years identified between 1970 and 2002. Concurrently, we found abrupt changes in mean spring and winter air temperatures, winter precipitation, and large‐scale climate oscillations that occurred either the same year or 1 yr prior. Earlier ice breakup and the shortening of the ice season will have consequences for winter heritage, local economies, and lake ecosystems around the world.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.346

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)0.0000.000

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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.

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

Citations74
Published2019
Admission routes2
Has abstractyes

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