Profitability analyses of Québec dairy cattle using health and management data via visualization tools.
Bibliographic record
Abstract
Data routinely collected from Dairy Herd Improvement (Québec DHI) were combined with provincial veterinary-health data with the objectives of 1) creating an integrated dataset with lifetime cumulative variables, 2) developing an analysis of different factors affecting lifetime profitability in dairy cattle using an empirical approach and 3) creating a tool to analyze profitability at the herd and individual levels using an information visualization methodology.For the lifetime profitability analysis, all animals were required to have complete data and, to maximize the validity of the analysis animals were selected from herds that routinely recorded health events.Profitability formulae from different sources reported in the literature were tested with the empirical data to study their potential applicability as decision tools for herd managers.It was found that when used in combination, Cumulative Lifetime Profitability (LTP) and Cumulative Lifetime Profitability Adjusted for the Regressed Opportunity Cost of the Postponed Replacement (LTPOC) could provide decision makers with a more complete understanding of the profitability of an animal by analyzing its individual performance and its marginal contribution to the herd.Using the selected profitability measures, a comparative analysis of differences in profit associated with common housing and milking systems in Québec showed that in terms of milking systems there were significant differences in profitability due to the milk production revenues, the cost of age at first calving and the costs of health.Profitability results and variables that showed significant differences among the housing and milking systems, such as cumulative health costs, were transformed into visualization curves benchmarks (means and top 90 and bottom 10 percentiles distribution ranges), which demonstrated that profitability and viii
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.010 | 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".