Technical note: Validation of methodology for characterization of feeding behavior in dairy calves
Bibliographic record
Abstract
Time sampling techniques are useful in collecting feeding behavior data because they minimize the time required for observation. Instantaneous recording is often used in the collection of feeding behavior data for dairy calves; however, the recording intervals used vary widely and it is unclear what minimum interval is necessary to yield accurate data. The objective of this study was to validate data obtained using instantaneous recording of feeding behavior of dairy calves across a range of time intervals with data obtained from continuous recording. Ten Holstein bull calves were observed continuously using time-lapse video for 3 d during the milk-feeding period while they were fed milk ad libitum and for 3 d post-milk-weaning while they consumed solid feed. Feeding behavior data obtained from continuous recording were compared with data obtained from instantaneous recording at intervals ranging from 15 s to 10 min. As expected, the strength of linear association between behavior measures obtained from continuous recording and instantaneous recording decreased with increasing recording interval. The relationship varied between feeding behavior measures; feeding time was represented well (R² >0.76) by instantaneous recording at up to 5-min intervals, but a strong linear association of meal frequency and meal time (R² >0.8) required intervals no greater than 1 min and 30 s, respectively. The relationship between feeding behavior measures obtained from continuous recording and recording at different intervals was similar in both periods; however, sensitivity of time sampling data across recording intervals was greater during the milk-feeding period. Sensitivity was low in both periods (<0.7 with scanning intervals as short as 1 min), indicating that instantaneous recording may not represent within-meal feeding behavior well. Instantaneous recording can provide accurate calf feeding behavior data if the recording interval is sufficiently short.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".