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Record W4410790393 · doi:10.1016/j.dib.2025.111710

Updating “Sub-hourly water temperature data collected across the Nechako Watershed, 2019–2021” to 2024 and with supplemental sites

2025· article· en· W4410790393 on OpenAlexaffabout
Justin Kokoszka, Dylan Broeke, Finn Calder-Sutt, Mostafa Khorsandi, Maria A Tavares, Stephen J. Déry

Bibliographic record

VenueData in Brief · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsWatershedResearch articleEnvironmental scienceHydrology (agriculture)Computer scienceGeologyLibrary science

Abstract

fetched live from OpenAlex

Nechako Watershed of northern British Columbia, Canada. Compared to our prior effort, we expanded the number of measurement sites from 25 in 2021 to 32 in 2024. Both previous and new sites record water temperature in the regulated part of the Nechako Watershed and many of its unregulated tributaries. The updated dataset is fully quality-controlled and homogenized across all sites. This dataset relies on a network of in-situ monitoring stations initiated in 2019 across the Nechako Watershed. To date, 32 stations collect water temperature at 15-min intervals for three lakes, 11 creeks, and 18 river sites. Data collection for all sites is generally year-round, which captures extended periods near or at the freezing mark. The associated metadata reports the station's condition, any issues, the duration of the collection, and concerns/recommendations for the data analysis. The updated dataset can be used for establishing the impacts of hydrometeorological extreme events on water temperatures [3], hydrothermal modeling for climate change studies [4,5], assessing the efficacy of the Nechako River's Summer Temperature Management Program [6], and for water quality and aquatic habitat suitability analyses [7,8].

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 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
Published2025
Admission routes2
Has abstractyes

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