Short communication: Use of the Clegg hammer measure as an indicator of stall-surface compressibility in tie-stall housing and its relationship with stall comfort
Bibliographic record
Abstract
Stall base and stall surface (i.e., with or without bedding) are key risk factors in cow comfort in dairy herds. In Canada, rubber mats, concrete floors, and mattresses are the most common stall bases used in tie-stall systems. Straw, wood shavings, and sand, at variable depths, are the most commonly used type of bedding. The Clegg hammer (Clegg Impact Soil Tester Hammer; Lafayette Instrument Company, Lafayette, IN) is a tool used by engineers to test the compressibility of pavement or golf course surfaces. Recently, this tool has also been used to measure the compressibility of the stall surface on freestall dairy farms. A total of 32 tie-stall dairy herds were selected in Québec to test the usefulness of the Clegg hammer as a tool to assess stall surface compressibility in tie-stall housing. This study had 2 main objectives: (1) identify the location and the number of measurements needed to obtain a stable indicator of compressibility, and (2) identify differences in the compressibility of the stall surface depending on the stall base and bedding depth. On each farm, we tested the compressibility of 10 stalls. No significant differences were found between the front and the back of the stall for the location of the Clegg hammer measures. The differences in readings of the Clegg hammer were nonsignificant after the third measure taken at the same location, meaning that 3 measures are sufficient at one location to obtain a compressibility measure. Significant differences were found among the different stall base and surface combinations tested. Rubber mats were less compressible than mattresses. When a large quantity of bedding (>7.5 cm) was added on top of rubber mats, the compressibility results were equal to those of mattresses ≥10 yr old without bedding. To appropriately test the compressibility of stall surface in tie-stall farms, we recommend measuring the compressibility of the stall base on its own and with the usual amount of bedding used on the farm. Our study establishes that both stall base and surface affect compressibility, and that a large quantity of bedding helps increase the compressibility of the bed, especially on a harder stall base.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".