Effects of multiple view angles on the classification of forward-modeled MODIS reflectance
Bibliographic record
Abstract
This paper examines the effects of multiple view angles on the classification of forward-modeled, high-quality, multispectral reflectances in a Canadian boreal forest region using a decision tree classifier (C4.5). Bidirectional reflectance factors (BRFs) from the seven-band moderate resolution imaging spectroradiometer (MODIS) are reproduced from high-quality composite model parameter datasets that were retrieved using a daily rolling version of an operational algorithm developed for direct broadcast and that were successfully used in earlier research. To assess the classification accuracies, we adopted descriptive and statistically rigorous techniques based on a confusion matrix and a 10-fold cross-validation method. The results show that the classification accuracies derived from the modeled MODIS BRFs in the principal plane are not substantially different, with the exception of a few directions, relative to bi-hemispherical reflectances (the white sky albedo) in the MODIS bidirectional reflectance dis...
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".