Estimating optimal observational sampling frequency of behaviors for cattle fed high- and low-forage diets
Bibliographic record
Abstract
Video recordings of behavioral activities including eating, ruminating, drinking, standing, and lying were monitored to determine the minimum number of sampling days and sampling frequency required to obtain reliable estimates of these behaviors. Eight continental crossbred heifers, individually housed in a tie-stall barn with total mixed ration provided once per day, were divided by BW into two blocks and assigned to each of the two dietary treatments in a crossover design: high-forage diet (HF, forage:concentrate ratio 70:30) and low-forage diet (LF, forage:concentrate ratio 30:70). The cows were monitored continuously using a digital video recording system for 6 d in each of the two periods and a trained observer manually recorded the behavioral activities by minute. Mean time spent performing each behavior for 6 d with instantaneous samples of 1 min was compared with those obtained using fewer sampling days (1, 2, 3, 4, and 5 d) and less frequent scanning (2, 3, 4, 5, 10, 15, 30, and 60 min) using linear regression analysis, and the minimum number of sampling days or frequency was determined. Diet did not affect the accuracy and precision of predicting behavior from video recordings of the cows. When sampling days and scanning intervals were combined, the minimum recommended sampling frequency for accurately estimating a specific set of behaviors of beef heifers in tie stalls was: eating, 2 d with 4-min intervals; ruminating, 3 d with 4-min intervals; drinking, 2 d with 3-min intervals; and standing and lying, 2 d with 15-min intervals. Increasing sampling frequency beyond these minimums further enhanced the accuracy and precision of predictions. The total time of each behavioral activity was different between cows fed HF and LF diets with eating, ruminating, total chewing, and standing of heifers fed the HF diet greater (P < 0.01), but the lying (P < 0.01) and drinking time (P = 0.028) of heifers fed the LF diet greater. Meal patterns were different (P < 0.05) between the treatments except when meal size (kg) was expressed as DM or OM intake. For rumination patterns, the mean or maximum bout length (min/bout) was greater, but the maximum or minimum length of time heifers took to ruminate after eating was lower for the HF diet. From a practical standpoint, it is recommended to use 3 days of observations scanned at an interval of 4 min to reliably estimate the behavior of cows. If the focus is only lying and standing time, then 2 days with 15 min intervals can be used.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".