Using 137 Cs as a tool for the assessment and the management of erosion/sedimentation risks in view of the restoration of the Rainbow Smelt (Osmerus mordax) fish population in the Boyer River basin (Québec, Canada)
Bibliographic record
Abstract
The Boyer River (Quebec, Canada) drains a 217 km 2 watershed that is under cultivation at 60%. The last 2 km of the river bed has always been used as a spawning ground by Rainbow Smelts (Osmerus mordax). This fish population, which plays an important ecological role in the St.Lawrence River estuary, has dramatically declined over the last decades. Siltation and excessive algal growth in the spawning area were identified as the most probable causes of the fish population decline; suggesting that soil erosion, nutrient and sediment transport are major factors underlying the environmental problem . In this context, 137Cs provides an effective tool for investigating the magnitude and spatial distribution of long-term soil redistribution taking place in the watershed. Sampling of cultivated fields, riverbanks, bottom sediments and forested sites were thus undertaken to help understand the erosive behaviour of the watershed. Results obtained so far suggest in-field erosion rates of up to 13 t ha-1 yr-1 with net outputs reaching 11 t ha-1 yr-1. These results agree well with estimates obtained from the USLE. The 137Cs data indicate that fields located in the upstream half of the basin produce smaller sediment loadings than those in the downstream portion, despite higher soil erodibilities and more frequent ose for annual crops. They also suggest that more than 75% of the sediment deposited in the spawning area originates from cultivated fields, and less than 25% from streambanks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".