An Automated Hardware-Software Module Monitoring Acheta Domesticus Population at Breeding Facilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing population on planet Earth and the deteriorating environment are leading humanity to a swift depletion of resources. And if it is possible to reduce the use of some, it is impossible to eliminate, or even decrease the consumption of protein. Thus, an alternative solution needs to be found. For the past several decades scholars have suggested to breeding crickets as an alternative source of protein. Numerous studies have been made, which resulted in a simple description of the process and a manual of how to establish a breeding cricket farm. However, the fluctuations in breeding conditions stemming from the lack of automation in this sphere, are a hazard to the safe growth and development of the cricket breeding stock. This paper focuses on the developed prototype of a video monitoring equipment developed using machine learning technologies aiming to help identifying hazardous conditions based on the training received in the process of the experiment and numerous tests. The prototype has shown a 70% accuracy rate, yet is capable of determining when the crickets are subjected to various stressors, namely water, nutrition, thermal and methane. Via observing the cricket population, the prototype is learning to alert the breeder as to the potential danger, thereby preserving the cricket population, and increasing the chances of a future mass production of protein from crickets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it