Eigenspectra for flocculation quality estimation
Bibliographic record
Abstract
Abstract We present an image analysis algorithm for flocculation quality estimation in high‐solids slurries, and demonstrate its performance using inline process images of oil sands tailings flocculation. While a skilled human operator can often successfully evaluate such images, variations in feed as well as the lack of isolated flocs or spatial reference‐points inherent in a high‐solids slurry can cause conventional image analysis techniques to fail. We overcome these challenges by recasting the images in Fourier space, discarding phase information, and applying an eigenfaces‐inspired image recognition algorithm to the resulting spectra. Each image is represented using a few projection coefficients onto an orthogonal basis and evaluated using likelihood‐based classification schemes. This algorithm shows a high degree of success evaluating the flocculation quality of 129 batch and inline flocculation experiments (5,610 images total) utilizing feed tailings from two different oil sand producers at a variety of feed dilutions and flocculant dosing levels.
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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) | 0.000 | 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) | 0.000 | 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 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".