Multiyear variations in High Arctic river temperatures in response to climate variability
Bibliographic record
Abstract
Water temperature measurements (2004–2016) from two small rivers in the High Arctic were analyzed to determine the effects of climate variability on thermal regime and the sensitivity to climate change. The East and West rivers (unofficial names) drain similar watersheds (11.6 and 8.0 km2, respectively) and are located at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Canada (74°55′N, 109°35′W). Differences in seasonal timing of river temperatures were evident when comparing the coldest and warmest years of the study period, and across different discharge conditions. Snowmelt runoff is characterized by uniformly cold water (∼0–1 °C) over a wide range of discharge conditions, followed by warming water temperatures during flow recession. The rivers showed varying sensitivity to mid-summer air temperature conditions in a given year, with warmer years indicating high correlation (r2 = 0.794–0.929), whereas colder years showed reduced correlation (r2 = 0.368–0.778). River temperatures reached levels which are reported to negatively affect fish and other cold-water aquatic species (>18 °C) with greater frequency and duration during the warmest years. These results provide a basis to further enhance prediction of river thermal conditions to assess ecosystem health in a river system and to refine insights into the effects of climate change on High Arctic aquatic ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".