Fish biodiversity and morphological quality in small agricultural streams of Monteregie, Quebec
Bibliographic record
Abstract
Stream channelization and modification is a widespread practice on agricultural land in Monteregie, Quebec, however, it is well known that channel simplification reduces the variability of instream habitat complexity and affects the biodiversity of these streams. Despite over 30,000 km of streams being subjected to channelization-type development in Quebec, very little is known about the extent of stream degradation and effects it may have on local geomorphology and ecology. The Morphological Quality Index (MQI) is a tool used to measure a stream’s hydrogeomorphological quality and has been shown to be a reliable predictor to help assess habitat quality in small headwater streams. The purpose of this research is to determine the health of fish communities in small streams in Monteregie and assess whether there is a significant relationship between biological communities and the MQI. Over 1,220 fish samples and 85 stream reaches with drainage areas less than 130 km2 were analysed in Monteregie. Results showed that small streams in Monteregie were higher in fish biodiversity than expected, with clear relationships between the fish metrics and proportion of land use in forested or agricultural categories. The MQI was also able to predict biological communities with up to an R2 = 0.50, which shows that the MQI could be used as a reliable tool to efficiently assess streams while providing insight on how stream modification affects overall biodiversity in agricultural watersheds.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".