Identification of selected log characteristics from computed tomography images of sugar maple logs using maximum likelihood classifier and textural analysis
Bibliographic record
Abstract
Abstract In recent years, computed tomography (CT) was investigated to acquire internal log information non-destructively. This paper studied the feasibility of identifying internal log characteristics in CT images by means of maximum likelihood classifier. The log characteristics to be identified include heartwood, sapwood, inner bark, and knots in sugar maple. A total of 100 CT images were sampled from one log to develop the classifier and 20 images were selected from another log for validation. Besides spectral and distance features, textural features were also assessed. In total, nine of them were selected as the input features for the classifier based on the class separability analysis. The classifier developed in this study produced overall accuracies of 79.8% and 72.2% for the training images and the validation images, respectively. This study indicates that the developed maximum likelihood classifier relying on a combination of spectral, textural, and distance features may be applicable to identify the internal log characteristics in the CT images of sugar maple.
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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) | 0.001 | 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) | 0.001 | 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 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".