Updating “Sub-hourly water temperature data collected across the Nechako Watershed, 2019–2021” to 2024 and with supplemental sites
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".