Prediction of land‐use conversions for use in watershed‐scale hydrological modeling: a Canadian case study
Bibliographic record
Abstract
Abstract Land‐use conversion models elucidate the complexities and spatial interdependencies of components of land use systems and provide insights into future land‐use configurations. In this paper, the 2012–2050 future land‐use patterns in the South Nation (SN) River basin, located in eastern Ontario, Canada, were generated with a modified version of the CLUE model and a 2011 reference map. The SN is an example of a basin where some water quality endpoints have dropped below acceptable limits because of a combination of intensive agriculture, urbanization, and climate change. Five historical land‐use maps were used to identify the historical trends in generalized land‐use classes. Seven demographic and geographic factors were used to derive the spatial distribution of land suitability to each land‐use class. The methodology was first validated by simulating land‐use changes from 1991 to 2011 starting from the 1991 reference map, and comparing the simulated 2011 map to the 2011 reference map. Then, the 2012–2050 land‐uses were generated, assuming historical trends derived from historical reference maps will continue in the future. Environmental impacts of the projected land‐use changes were discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".