Bibliographic record
Abstract
iii Spectral features within satellite images change so frequently and unpredictably that spec-tral definitions of land cover are often only accurate for a single image. Consequently, land-cover maps are expensive, because the superior pattern recognition skills of human analysts are required to manually tune spectral definitions of land cover to individual im-ages. To reduce mapping costs, this study developed the Template-Guided Classification (TGC) algorithm, which classifies land cover automatically by reusing class information embedded in freely available large-area land-cover maps. TGC was applied to map rem-nant forest within six 10-m resolution SPOT images of the Vermilion River watershed in Alberta, Canada. Although the accuracy of the resulting forest maps was low (58 % forest user’s accuracy and 67 % forest producer’s accuracy), there were 25 % and 8 % fewer er-rors of omission and commission than the original maps, respectively. This improvement would be very useful if it could be obtained automatically over large-areas. iv Acknowledgments I am grateful for the opportunity to explore the ideas presented in this thesis and for the people that have supported me. I especially thank Dr. Karl Staenz for his patient support, Dr. Jinkai Zhang for sharing his remote sensing experience and for his friendship, Dr. Craig Coburn for his advice and practical help in the face of looming deadlines, and Dr. Howard Cheng for his dutiful oversight. I appreciate their generosity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.548 | 0.370 |
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".