Detecting change in disturbed areas in Grasslands National Park using remote sensing techniques
Bibliographic record
Abstract
Grasslands National Park was established in 1984, when it began to acquire land from local land owners as it became available for sale. One of the purposes of this park was to create an area where native prairie can be restored and conserved. Because the park is concerned with creating a natural prairie ecosystem, the extent and spread of introduced species (disturbed areas), is of interest. In response to this interest, the first objective of this project is to analyze the spatial distribution of change between 1984 and 2001 in \ndisturbed areas within the West Block of Grasslands National Park using remote sensing techniques. The second objective is to evaluate which vegetation indices were best suited to map vegetation change between 1984 and 2001. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Simple Ratio (SR) were calculated for both the 1984 and 2001 satellite data, and then each of the vegetation index images were subtracted from one another to reveal the change that had occurred between the two dates. Results showed that the species Summer-cypress was often associated with negative change; while Smooth Brome was possibly associated with areas of positive change, particularly in the north-east corner of the park where the Frenchman River crosses the park boundaries. It was found that NDVI was the best vegetation index to map change in disturbed areas of Grasslands National Park in valleys and high moisture areas, while the SAVI was best suited for dry, upland areas.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".