The Ecology of River Ice
Bibliographic record
Abstract
Abstract Many of the world's rivers are ice‐covered during winter months but increasing evidence indicates that the extent of river ice will shift substantially as winters warm. However, our knowledge of rivers during winter lags far behind that of the growing season, limiting our understanding of how ice loss will affect rivers. Physical, chemical, and biological processes change from headwaters to large rivers; thus, we expect ice processes and resulting effects on the ecology of rivers could also vary with river size, as a result of the associated changes in geomorphology, temperature regimes, and connectivity. To conceptualize these relationships, we review typically disparate literature on ice processes and winter ecology and compare what is known in the smallest and largest rivers. In doing so, we show that our ability to link ice with ecology across river networks is made difficult by a primary focus on ice processes in larger rivers and a lack of study of ecosystem processes during winter. To address some of these gaps, we provide new scenarios of river ice loss and analyses of how the annual importance of winter gross primary productivity (GPP) varies with river size. We show projected ice loss varied with large‐scale watershed characteristics such as north‐south orientation and that the importance of winter to annual GPP was greatest in the smallest rivers. Finally, we highlight information needed to fill knowledge gaps on winter across river networks and improve our understanding of how rivers may change as climate and ice regimes shift.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".