Modulation of linear and nonlinear hydroclimatic dynamics by mountain glaciers in Canada and Norway: Results from information-theoretic polynomial selection
Bibliographic record
Abstract
Historical streamflow and climate datasets were analyzed for low- and high-frequency hydroclimatic variability. Four glacial/non-glacial catchment pairs were considered, two from the southern Canadian Rocky Mountains and two from arctic coastal Norway. Analyses were performed using daily data, providing high seasonal resolution and facilitating the identification of possible nonlinear hydroclimatic processes. Spearman rank correlation, and an information theory-based polynomial selection method, were employed in parallel. The latter permits straightforward identification of highly nonlinear relationships, simultaneous consideration of multiple models and estimation of the probability of a given relationship, as distinct from conventional p-values. Highly nonlinear (parabolic) atmospheric teleconnections to the Arctic Oscillation and El Niño-Southern Oscillation were confirmed in Norway and Canada, respectively, and their corresponding hydrologic effects were detected; conversely, little evidence for deviation from linearity was found for long-term monotonic trends. Presence or absence of watershed glacial cover was found to fundamentally alter streamflow responses to climate variability and change. In particular, for ecologically highly relevant late-summer low flows, glaciers induced: (1) stronger negative long-term trends than observed for non-glacial basins, presumably reflecting net mass balance declines seen in nearby glaciers, and (2) parabolic teleconnections, largely absent in non-glacial basins, reflecting parabolic air temperature teleconnections and the presence or absence of glacial ice available for melting.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".