An Evaluation of LANDSAT TM Data and GIS Modelling To Identify Significant Woodlands in Southern Ontario
Bibliographic record
Abstract
The rapid and reliable identification of woodlands that should be protected from incompatible development is an urgent need in municipal planning to secure a viable natural heritage system. The objective of this research was to test the use of LANDSAT Thematic Mapper (TM) spectral data as a current, unified, and scalable thematic layer to identify ecologically significant woodlands in southwestern Ontario. LANDSAT TM data were classified to obtain a Treed Cover data layer for input into a geographic information system (GIS) model that integrated conventional mapping layers (topography, hydrology, soils, vegetation types); patch metrics (size, shape); and landscape connectivity (proximity, linkages). The Treed Cover layer obtained from the LANDSAT TM data provided a reliable representation of woodland patches when compared to other sources. The integrated data were tested against ecological criteria to identify candidate patches for a preliminary representation of significant woodlands. The GIS model was tested for wildlife habitat conservation planning at the landscape scale using forest area sensitive bird species data and interior habitat data obtained from the Treed Cover layer.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".