Quantifying landscape structure with vegetation inventory maps and remote sensing
Bibliographic record
Abstract
Two input maps based on Alberta Vegetation Inventory (AVI) data and Landsat satellite imagery were generated for use in a fragmentation analysis of a large area in the Alberta Yellowhead Ecosystem to support long-term grizzly bear habitat analysis. Accuracy was assessed using visual interpretation of classes on digital orthophotography. Approximately 45% map accuracy was obtained after applying a generalization procedure to the available AVI GIS database. Approximately 80% map accuracy was achieved used a supervised classification approach applied to the Landsat image. Differences in accuracy were most apparent in non-treed vegetation classes (e.g., shrub), closed conifer, mixedwood and deciduous forest classes. Very large differences were observed in many of the landscape metrics computed from these two maps to quantify landscape structure. Simulating forest changes on these maps illustrated the difficulty of comparing maps generated with different geospatial technologies. Key words: fragmentation, satellite remote sensing, GIS vegetation inventory maps, landscape metrics
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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 | 0.000 |
| 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 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".