Bibliographic record
Abstract
In order to estimate the amount of oil that can be recovered from oil sands slurry, technically referred to as processability number, we propose a method based on image processing in this paper. Our study begins with a review of human observations in conducting this task to determine visual features pertinent for assessing ore slurry quality. Subsequently we extract potentially useful image features and use them to train a regressor to learn the relationship between the visual features and the ore quality. Specifically, an input image is first divided into three layers representing different materials in the slurry through image segmentation. Image features in the bottom two layers are then extracted. We create three types of features - grayscale features, Haralick features and the power spectrum - to evaluate their ability to predict the processability number. For this purpose, a regressor model is trained using Adaboost with one or more types of the visual features as input. Experimental results show that the Haralick features provide the best estimate of the ore quality in terms of the procesability number, and that it is possible to design an automated system for assessing the quality of an industrial product through image processing techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".