Short communication: An observational study investigating inter-observer agreement for variation over time of body condition score in dairy cows
Bibliographic record
Abstract
Body condition score (BCS) is strongly correlated with energy reserves. The ease, rapidity of scoring, and high intra- and inter-observer repeatability make it a widely used herd management tool in bovine practice and in scientific studies. Loss or gain of BCS, rather than a single BCS measurement, is frequently used to monitor energy balance in dairy cows. It is unknown if the difference between 2 BCS measures taken at different moments (ΔBCS) would demonstrate inter-observer agreement similar to that of a single BCS measurement. The objective of this study was to compare inter-observer agreement of BCS and ΔBCS in dairy cows when multiple observers perform data collection. An observational study was conducted between April and September 2015; 3 observers independently assessed BCS of 73 Holstein cows from 1 commercial dairy herd. Body condition score assessments of the animals were performed between 1 and 20 d in milk (early lactation; exam 1) and again between 41 and 60 d in milk (peak of milk production; exam 2). Quadratic weighted kappa (κ w ) was computed to quantify agreement between observers for single BCS measurements and ΔBCS. For single BCS measurements, κ w of 0.79 (95% CI: 0.69, 0.85) and 0.84 (95% CI: 0.77, 0.89) were obtained for exam 1 and exam 2, respectively. Such values would be interpreted as strong agreement and are consistent with the available literature on BCS repeatability. When computing agreement for ΔBCS, a κ w value of 0.49 (95% CI: 0.32, 0.63) was obtained, suggesting moderate agreement between observers. These findings suggest that studies investigating single BCS measures could use many observers with a high degree of accuracy in the results. When ΔBCS is the parameter of interest, more reliable results would be obtained if one observer conducts all assessments.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".