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Record W2613265511

Lake Temperatures as Sentinel Responses to Climate Change

2015· article· en· W2613265511 on OpenAlexfundno aff
Rachel M. Pilla

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersGlobal Lake Ecological Observatory NetworkUniversity of Miami
KeywordsClimate changeUrban heat islandClimatologyEnvironmental sciencePhysical geographyGeographyGeologyMeteorologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

The surface waters of two small lakes in northeastern Pennsylvania have warmed over the past 27 summers , but with no significant increase in air temperature, the quintessential driver of lake surface warming.I assessed long-term trends and interannual relationships between lake thermal structure and regional meteorological patterns to determine the changes in and drivers of lake temperatures.Strong increases in thermal stratification accompanied surface water warming trends in both lakes.In the clearer lake, I found cooler deepwater temperatures over time contributing to net whole-lake cooling.Precipitation significantly increased over this time period and was most strongly correlated to the changes in lake thermal structure.These changes are largely related to precipitation-driven increases in dissolved organic matter in the lakes, leading to reductions in water transparency driving thermal responses.These changes to lake ecosystems have important ecological consequences, including changes in physical processes and vertical habitat gradients.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.211
Teacher spread0.194 · 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

Citations0
Published2015
Admission routes1
Has abstractno

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