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Record W4385633954 · doi:10.1002/lol2.10349

Thermal regimes of groundwater‐ and lake‐fed headwater streams differ in their response to climate variability

2023· article· en· W4385633954 on OpenAlexafffundabout
Danielle T. Hudson, Jason A. Leach, Daniel Houle

Bibliographic record

VenueLimnology and Oceanography Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityEnvironment and Climate Change CanadaNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceEnvironment and Climate Change Canada
KeywordsSTREAMSEnvironmental scienceHydrology (agriculture)WatershedClimate changeGroundwaterEcosystemSpring (device)Aquatic ecosystemFreshwater ecosystemEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Stream thermal regimes are being altered by climate change with consequences for aquatic organisms. Most documented long‐term changes in stream temperature are from large rivers. We know less about water temperature trends for small headwater streams, especially those found in northern landscapes that contain small lakes. We analyzed 36 yr of stream temperature observations from a long‐term watershed study in Ontario, Canada, to understand how headwater streams are responding to climate variability. We found that groundwater‐fed (GWF) and lake‐fed (LF) streams exhibit contrasting responses, as GWF streams warmed in the spring (0.19–0.60°C per decade) and LF streams warmed in the fall (0.39–0.72°C per decade). Both stream types exhibited weak temperature trends in summer and winter. These results highlight that a stream network perspective that includes headwater streams and small lakes, and accounts for seasonal changes in thermal regimes, is important for understanding aquatic ecosystem response to climate variability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.434

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 studies0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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