Comment on egusphere-2025-1971
Bibliographic record
Abstract
Abstract. Northern landscapes are enduring ongoing impacts of anthropogenic land use and climate change. Rivers are valuable indicators of this change reflected by the timing and amount of water and terrestrial material they mobilize. Assessing the influence of a changing climate on diverse river systems is best achieved using multi-annual monitoring and replication of effort across varied tributary catchment conditions. We used this approach to monitor concentrations, catchment yields, and export of total suspended sediments (TSS) and dissolved organic carbon (DOC) of a large, diverse, glacial river network (North Saskatchewan River; NSR) in western Canada during years of extensive weather fluctuations. Though concentrations of TSS and DOC increased eastward through the NSR basin from Rocky Mountain cordillera to agriculturalized plains, catchment yields were statistically highest from cordillera regions, reflecting an eastward rain shadow. Wet conditions across the basin resulted in variable but statistically higher TSS and DOC yields compared to drought conditions. During higher water, we observed disordered, threshold-type, erosive mobilization of TSS through the basin whereas DOC increased more predictably with runoff. Variability of yields and export was substantial both within and between pristine and impacted catchments, and within the NSR mainstem illustrating the complexity of river sediment and organic matter transport at the network scale. Consequently, in a warming and wetting climate, we expect TSS and DOC transport to intensify with sediment transport being more difficult to predict compared to organic matter, which has implications for aquatic ecosystems and >1.5 M people who depend on the NSR for drinking water.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.012 |
| Insufficient payload (model declined to judge) | 0.210 | 0.161 |
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".