Automated measurement of changes in feeding behavior of milk-fed calves associated with illness
Bibliographic record
Abstract
There is a need for improved methods of detecting illness among group-housed milk-fed calves. In 4 separate experiments, we examined whether illness in group-housed dairy calves fed with an automated milk feeder changed their feeding behavior, and whether these changes were affected by low (n = 26) or high (n = 38) milk rations. All calves were subjected to regular health checks that included general condition, rectal temperature, lung auscultation, and fecal scoring. We match paired calves that succumbed to illness with healthy calves on the same feeding allowance. In the days following clinically identified illness (gastroenteric or respiratory affections), sick calves fed high allowances of milk or milk replacer decreased milk intake (-2.59 +/- 0.7 L/d) and frequency of visits to the milk feeder (-2.43 +/- 0.3 visits/d), and increased the duration of each visit to the milk feeder (1.66 +/- 0.5 min/visit) compared with healthy calves fed at the same allowance. However, sick calves fed a low allowance of milk or milk replacer only decreased the duration of each visit to the milk feeder (-1.35 +/- 0.2 min/visit) compared with healthy calves. Feed allowance affected feeding behavior associated with illness of milk fed calves.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".