Sustainable Ice-Jam Flood Management for Socio-Economic and Socio-Ecological Systems
Bibliographic record
Abstract
Ice jams are critical components of the hydraulic regimes of rivers in cold regions. In addition to contributing to the maintenance of wetland ecology, including aquatic animals and waterfowl, ice jams provide essential moisture and nutrient replenishment to perched lakes and ponds in northern inland deltas. However, river ice-jam flooding can have detrimental impacts on in-stream aquatic ecosystems, cause damage to property and infrastructure, and present hazards to riverside communities. In order to maintain sustainable communities and ecosystems, ice-jam flooding must be both mitigated and promoted. This study reviews various flood management strategies used worldwide, and points to the knowledge gaps in these strategies. The main objective of the paper is to provide a framework for a sustainable ice-jam flood management strategy in order to better protect riverine socio-economic and socio-ecological systems. Sustainable flood management must be a carefully adopted and integrated strategy that includes both economic and ecological perspectives in order to mitigate ice-jam flooding in riverside socio-economic systems, while at the same time promoting ice-jam flooding of riverine socio-ecological systems such as inland deltas.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".