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Record W7084623396 · doi:10.5281/zenodo.17259603

Distinct thermal regimes drive contrasting heatwave responses in alpine and montane peatlands of the Canadian Rockies

2025· dataset· en· W7084623396 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPeatMontane ecologySphagnumWater tableHydrology (agriculture)Table (database)BogRange (aeronautics)

Abstract

fetched live from OpenAlex

This study assesses how peatlands situated at elevational extremes of their Canadian Rocky Mountain range differ in thermal dynamics and vulnerability to temperature extremes, using in situ data collected before, during and after the record-breaking 2021 heatwave. The study sites include Helen fen (High site), (2365 m, 1.2 ha, 51°40’59.98”N, 116°24’32.9”W), situated above tree line, and Sibbald fen (Low Site) (1480 m, 130 ha, 51°03’29.38” N, 114°52’11.66” W), located in the montane zone. Environmental data for these sites is provided. Study period September 2019 to March 2022. No recording indicated by NA. Datasets: HelTempsWT2019-2022 and SibbTempsWT2019-2022 Time: Timestamp of measurement (DD/MM/YYYY HH:MM) AirTempC: Air temperature measured in °C every 15 minutes in Helen, every 30 minutes in Sibbald. WT_m: Soil water table depth from data logger in m, measured hourly. T_5cm – 70cm: Soil temperature at 5, 10, 20, 30, and 40 cm depth (°C) at Helen, plus 40, 50, 50, 70 at Sibbald, measured hourly. NR_W_m2: Net radiation (W m⁻²), every 15 minutes in Helen, every 30 minutes in Sibbald.

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)0.004
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.394
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0040.003
Research integrity0.0000.001
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 head0.053
GPT teacher head0.320
Teacher spread0.267 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes2
Has abstractyes

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