Bibliographic record
Abstract
Abstract This paper summarizes the development of equipment and techniques used for automated capture of floc images and measurement of floc properties. Methods for floc analysis are described which may be used equally well in the field or in the laboratory. The Matlab Image Processing Toolbox is used as a front end for applying several algorithms designed to clarify the images and measure the flocs. An iterative function provides threshold values to separate flocs from varying background light conditions in a stable and repeatable manner. The methods described can provide real-time analysis without user intervention. Matlab and IDL routines have been designed to measure settling velocity. The use of totally automated tracking over measurements made by a graphical user interface approach results in vastly more efficient measurements. The automated method also provides better data for very small and very slow-moving flocs. The graphical user interface approach, on the other hand, does not lead to some of the errors associated with the automated tracking. Both methods provide similar information about floc number, size, shape, and settling velocity, which can be used to calculate properties such as excess density, porosity, and fractal dimensions. Finally, kernel density estimation is introduced as a method to make meaningful interpretations from large amounts of highly variable data.
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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 | 0.003 | 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".