Tree ring analysis of hydro‐climatic thresholds that trigger ice jams on the Mistassini River, Quebec
Bibliographic record
Abstract
Abstract This study documents the spatiotemporal variations in the frequency and magnitude of ice jams in the Mistassini River and applies that information to the identification of the hydro‐climatic threshold conditions associated with major events. Ice jams cause severe upheavals in water flow, which result in flooding upstream of the ice jam front, and therefore represent a significant geohazard to riparian populations. To analyze the spatiotemporal variations in the magnitude–frequency of ice jams, the Mistassini River was first divided into six different sites representing different geomorphological contexts. A 50‐year ice jam chronology was constructed from 85 damaged trees from all of the study sites. This chronology was then coupled with hydro‐climatic variables to construct classification trees, which helped identify the conditions and hydro‐climatic thresholds favourable to the triggering of ice jams in a predictive model. The results indicate complex interactions between the characteristics of flow, ice cover and river morphology that affect the frequency and magnitude of ice jam events on the Mistassini River. These factors affect the frequency and magnitude of ice jam events. The triggering of extreme events seems particularly influenced by exceptional ice conditions and sites with high sinuosity and islands. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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) | 0.002 | 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".