Longevity of Nelore Cows of the Bolivian Tropics. Is It Possible to Explain It Through Productive Variables?
Bibliographic record
Abstract
The objective of this work was to evaluate the longevity and its relationship with productive variables of Nelore cows in grazing systems of the Bolivian tropics. Retrospective data were used corresponding to 259 Nelore breed cows, primiparous and multiparous discarded with a total of 800 births, in the period between 2005 and 2019, belonging to the Cooperativa Agropecuaria Integral San Juan de Yapacaní (CAISY) located in the San Juan Japanese Communities, Santa Cruz, Bolivia. The variables analyzed were: Live weight of cow (WC) in kg, Weight of the calf at birth (WCB) in kg, Weight of calf at weaning (WCW) in kg, Total weight of weaned calf (WWC) in kg, Age at first calving (AFC) in months, Number of calvings (NC), Longevity (L) in days, Calf Index (CI) in kg, Accumulated Productivity (PAC) in kg, Total calf production (CP) in kg, Efficiency of Stock (ES) in kg. In order to respond to the main objective of this work, the relationship between the life longevity of the cow and the other productive variables was studied. For this, first principal component analysis (PCA) was carried out, by means of which the space dimension of the productive variables was reduced creating new linearly independent variables and in this way avoid problems of multicollinearity in the model, because the productive variables in some cases turned out to be correlated. Then, the first three main components that explain 77% of the total variability of the data were retained and interpreted as follows: The PC1 was high and directly correlated with the variables NC, Kg produced total, % of stock efficiency, PAC and Kg produced meat / day, therefore can be thought of as an indicator of "productive efficiency". PC2 was an indicator of "efficiency in rebreeding" since it presented altar and direct correlations with WC and AFC. PC3 was high and directly correlated with birth weight and weaning weight, which is interpreted as an indicator of "breeding efficiency". Finally, a multiple linear regression model was adjusted considering Longevity as a function of productive efficiency, breeding efficiency and rebreeding efficiency (p-value <0.0001 in the three cases). 87% of the total variability of L (days) is explained by the model. It is concluded that Longevity is related to productive indicators for this group of Nelore cows in grazing systems of the Bolivian tropics.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".