Multispectral detection of European frog-bit in the South Nation River using Quickbird imagery.
Bibliographic record
Abstract
This thesis investigated multispectral detection of the invasive floating macrophyte, European Frog-bit, using Quickbird imagery and fuzzy image classification. To determine if the spectral\nsignature of European Frog-bit were separable from other wetland vegetation, a species level land cover classification was conducted on a 6km section of the South Nation River in Ontario, Canada. Supervised and unsupervised imagery classification approaches were evaluated using the fuzzy classifiers, Fuzzy Segmentation for Object Based Image Classification (FS) and Fuzzy\nC-Means (FCM). Both approaches were sufficiently robust to detect European Frog-bit. User’s and producer’s accuracies for the European Frog-bit class were 81.0% and 77.9% for the FS classifier and 63.5% and 73.0% for the FCM classifier. These accuracies indicated that the spectral signature of EFB was sufficiently different to permit detection and separation from other\nwetland vegetation and fuzzy image classifiers were capable of detecting EFB in Quickbird imagery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".