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
Post harvest stored grain losses remain a problem. Vigilant post-harvest grain management is the most cost-effective means of increasing the world's food supply. Spoilage of bulk-stored grain leads to decreased nutritional value and poses health hazards due to the formation of irritating volatile metabolites inside grain bins. Quality changes in the stored grain bulk can be identified by various odors as well as increase in carbon dioxide. This paper provides information and analysis about the potential of sensors for grain quality monitoring, a brief overview of the innovative research on the development of sensors and future perspectives. On the go grain quality monitoring gas sensors, electrostatic sensors for particle size measurement for grain dust, moisture, and acoustic sensors are identified as potential instruments to be employed inside the grain bin for monitoring the quality of grain and for increasing the shelf life of stored grain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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