Validation of Contact Mats to Monitor Dairy Cow Contact with Stall Partitions
Bibliographic record
Abstract
In indoor housing systems, repetitive contact with the stall partitions may reflect issues between the housing environment and the cow, by reducing the quality of rest and hindering ease of movement. The objective of this study was to validate the ability of a contact mat (CM) system to monitor cow contact with stall dividers and neck rail when compared to video observation. Eleven lactating cows were monitored using video recording and with the CM system for 4 h/d for 4 consecutive days. CM were affixed to the stall dividers and neck rail to record the frequency of cow contact per second. Two observers recorded the frequency of cow contact against the stall partitions per second using three criteria: cow contact with the stall dividers or neck rail regardless of CM contact (Total Rail Contact), cow contact with the CM regardless of placement or force (Total CM Contact), and cow contact with the CM through proper placement and force (Effective CM Contact). The correlation strength used to measure agreement between video observation and CM system for cow ranking based on degree of contact varied from moderate to very high (dividers: Total Rail Contact: rs = 0.68, Total CM Contact rs = 0.90, Effective CM Contact rs = 0.90; neck rail: Total Rail Contact: rs = 0.71, Total CM Contact rs = 0.66, Effective CM Contact rs = 0.58). CM can be used to accurately rank cows based on their frequency of contact with the stall dividers. This can be used to identify individual cows that need intervention for stall comfort risk factors, or to assess which indoor housing environments offer fewer constraints and better movement opportunities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".