Opportunities for ACI in PLF
Bibliographic record
Abstract
The fields of animal-computer interaction (ACI) and precision livestock farming (PLF) have emerged in parallel, distinct communities, but have many overlapping goals and concerns. PLF is concerned with the development of new technologies to improve livestock farming operations and outcomes, including improving the health and welfare of farm animals. However, unlike ACI, which has roots in the human-computer interaction field and thus incorporates many user-centred design methodological traditions, PLF research has emerged largely from engineering fields and is highly technology-focused. This work-in-progress paper discusses the opportunities to apply research and methodologies emerging from the ACI field to help improve the usability and overall utility of PLF technologies for both its human and animal users. We also discuss several ongoing projects from our research group that take this approach.
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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 | 0.045 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".