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Record W2946713204 · doi:10.1007/s10584-019-02465-y

Substantial increase in minimum lake surface temperatures under climate change

2019· article· en· W2946713204 on OpenAlexfundno aff
R. Iestyn Woolway, Gesa A. Weyhenmeyer, Martin Schmid, Martin T. Dokulil, Elvira de Eyto, Stephen C. Maberly, Linda May, Christopher J. Merchant

Bibliographic record

VenueClimatic Change · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersUniversity of ReadingNatural Environment Research CouncilEuropean CommissionSight Research UKGlobal Lake Ecological Observatory Network
KeywordsEnvironmental scienceClimate changeBiogeochemical cycleSurface waterClimatologyEcosystemGlobal warmingLake ecosystemAtmospheric sciencesMaximum temperatureSurface air temperatureHydrology (agriculture)Physical geographyEcologyOceanographyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

The annual minimum of lake surface water temperature influences ecological and biogeochemical processes, but variability and change in this extreme have not been investigated. Here, we analysed observational data from eight European lakes and investigated the changes in annual minimum surface water temperature. We found that between 1973 and 2014, the annual minimum lake surface temperature has increased at an average rate of + 0.35 °C decade −1 , comparable to the rate of summer average lake surface temperature change during the same period (+ 0.32 °C decade −1 ). Coherent responses to climatic warming are observed between the increase in annual minimum lake surface temperature and the increase in winter air temperature variations. As a result of the rapid warming of annual minimum lake surface temperatures, some of the studied lakes no longer reach important minimum surface temperature thresholds that occur in winter, with complex and significant potential implications for lakes and the ecosystem services that they provide.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.024
GPT teacher head0.243
Teacher spread0.219 · 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

Citations108
Published2019
Admission routes1
Has abstractyes

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