Interannual evolution of hydrosedimentary connectivity induced by forest cover change in a snow‐dominated mountainous catchment
Bibliographic record
Abstract
Abstract Hydrosedimentary connectivity is a key concept referring to the potential fluxes of water and sediment moving throughout a catchment. In forested catchments, these fluxes are prone to alterations caused by anthropogenic and natural disturbances. In this study, we modelled the interannual spatiotemporal evolution of hydrosedimentary connectivity influenced by forest cover change over the last four decades in the Mont‐Louis catchment, a medium snow‐dominated mountainous catchment in eastern Canada, which had 62% of its total surface affected by forest disturbances (mainly logging, but also wildfires and diseases) between 1979 and 2017. Using a geomorphometric index of connectivity (IC) and a historical forest cover database, we produced one IC map per year that considered anthropogenic and natural disturbances affecting the forest cover of the studied catchment. To account for vegetation recovery, forest disturbances were weighted with local hydrological recovery rates. Over the four decades, the mean IC of the Mont‐Louis catchment dramatically increased by 35% in response to different types of disturbances. The spatial evolution of IC over the whole catchment and at the sub‐catchment scale revealed that disturbance location has a strong influence on hydrosedimentary connectivity to the main channel. Our results also highlight the sharp contrast between IC computed from topography‐based impedance to those computed from vegetation‐based impedance. Forest disturbances appear to connect hillslopes with the hydrological network by producing pathways for sediment and water. Finally, the proposed reproducible framework could be useful for predicting the potential impact of harvesting and preventing damage to fish habitat and sensitive river reaches.
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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) | 0.001 | 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".