Modification and Industrial Applicability of a Temperature Probe Capable of Tracking Compost Temperature on a Random Particle Level
Bibliographic record
Abstract
It is generally accepted that exposure of all compost particles to temperatures ≥55°C for at least three consecutive days is a sufficient criterion for a compost to be considered hygienic. Nonetheless, there are no known studies confirming that routine composting operations consistently provide the conditions to meet this criterion. The objectives of this study were: (i) to develop a self-contained temperature probe capable of mimicking random particle behavior in compost and recording the temperature it is exposed to, while withstanding adverse operating conditions; (ii) to validate the probe's physical characteristics in a field-scale operation; and (iii) to assess the recovery of probes from a full-scale compost operation. Two field trials found the probes do behave like random particles and that the aluminum case adequately protected the probe's circuitry and cryovial. Another two trials were conducted to analyze probe recovery. Temperature probes were randomly introduced into a freshly built aerated static pile. In the first trial, 80 m3 of material was screened in one day and the probe recovery efficiency was 100%. In the second trial, screening of 440 m3 of material was completed in three days and 79% of the probes were recovered. The inability to achieve a recovery rate of at least 90% could be due to the high moisture content (≥50%) of the material being screened, the high fraction of oversized material, and, most importantly, the heavy reliance on visually locating the probes in the screen overs.
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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 | 0.001 |
| 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 | 0.001 | 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 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".