Changes in riparian habitats along five major tributaries of the saint Lawrence river, QuÉbec, Canada: 1964–1997
Bibliographic record
Abstract
Abstract This paper investigates spatiotemporal changes over a 30‐year period within the riparian landscapes of five major tributaries of the Saint Lawrence River (Québec, Canada). Aerial photographs from 1964(1967) for the Saint Maurice, Saint François, Richelieu, Batiscan and Yamachiche Rivers were compared with 1997 photographs to quantify changes in vegetation and land use. The riparian zones were divided into five cover types to create landscape cover maps: herbaceous vegetation, woody vegetation, agriculture, urban and water. The maps were converted into a geographic information system to quantify landscape changes, which were attributed to either natural processes, such as sediment deposition and bank erosion, or anthropogenic influences, such as human settlement. The results show that both natural and anthropogenic changes were limited over the 30‐year study period and that natural processes dominated over human‐caused impacts. Overall, riparian vegetation patches became more fragmented and isolated with time due to the disturbances. However, important differences were observed between and within rivers. Fluvial processes resulted in riparian vegetation losses on three rivers (Richelieu, Saint François and Batiscan) and gains on the other two rivers (Yamachiche and Saint Maurice) while anthropogenic influences resulted in vegetation gains on four rivers, except the Richelieu where gains and losses were equivalent. Thus, while riparian landscapes are sensitive to both fluvial and anthropogenic processes, there is great variability in sensitivity among the tributaries. By providing a better understanding of past changes, studies such as this can help make better predictions of future changes that are expected to occur with climate change. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".