Sub-hourly water temperature data collected across the Nechako Watershed, 2019-2021
Bibliographic record
Abstract
Water temperature is actively being monitored along the regulated Nechako River and some of its unregulated tributaries in northern British Columbia (BC) to determine how climate variability, climate change and flow regulation influence water temperatures. The Nechako Watershed, located mainly in the sub-boreal spruce biogeoclimatic zone, spans 47,200 km2 in area [1]. The regional climate experiences a prominent seasonal cycle in air temperature and precipitation, with subfreezing temperatures and snow accumulating during winter. Waterways therefore experience extended near 0°C water/ice temperatures during the winter season. The accumulation of snow yields snowmelt-generated peaks in discharge during the spring freshet period in unregulated tributaries [2]. Regional studies on climate reveal recent warming trends that are anticipated to persist through the 21st century, with a projected mean air temperature increase of ∼2°C by the 2050’s [3]. In response to warming air temperatures, regional water temperatures are also on the rise, with an average warming trend of 0.7°C from 1950 to 2015 [4]. Changing water temperatures are important in understanding ecological and environmental impacts on riverine systems, including aquatic species such as fish (e.g., sockeye salmon (Oncorhynchus nerka), rainbow trout (Oncorhynchus mykiss) and white sturgeon (Acipenser transmontanus), all endemic species to the Nechako Watershed), invertebrates, and micro-organisms. Managing water temperatures during the fish spawning season is crucial as elevated temperatures induce stress and affect reproduction success [5]. Starting with a pilot project in summer of 2019, we expanded in situ monitoring of water temperature to 29 sites; however, for the purposes of this paper, only data from 24 sites are included (some sites have limited data samples or loggers could not be retrieved in the field). Currently, 25 sites are fully operational and collecting data, all deployed to sample and record data at 15-minute intervals starting at the top of the hour. Site data collection occurs at minimum once annually (typically during summer/early fall), along with site and logger maintenance. Field notes are taken to identify any potential issues with data collection, such as loggers dewatering during low flows, duration of logger removal for data collection, and any other environmental concerns that should later be considered during data analysis. The assembled data are useful to build long-term time series of observed water temperatures within the Nechako Watershed and as a baseline for future projects. The data are also used to determine the effectiveness of the Summer Temperature Management Program at the Nechako Reservoir and Skins Lake Spillway [1]. Therefore, these data can be used in the future to better identify the optimal discharge from the reservoir to minimize ecological effects on the watershed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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 teacher head, 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".