Bibliographic record
Abstract
Abstract Numerous pre- and post-mortem factors, such as genotype, production system, growth promotants, diet, health events, stress, slaughter age and weight, carcass chilling, and ageing time, have been shown to impact beef production and final product quality. The objective of livestock phenomics is the systematic acquisition of high dimensional phenotypic data, which requires measuring phenomes as they change in response to genetic mutation and environmental influences. Due to the decrease in costs associated to genomics technology and related fields, researchers had to face the so called “phenomic gap”, a lack of sufficient, appropriate phenotypic data. Selecting phenotypes of interests, standardizing methodologies, developing high-throughput data collection systems, systematically recording environmental factors, and integrating bioinformatics are some of the challenges when developing a livestock phenomics program. Precision livestock farming aims at applying continuous, automated real-time monitoring systems to optimize livestock management. The information collected by these systems can be used to optimize individual animal health and welfare, reproductive traits, and productivity, as well as environmental influences. This approach requires the use of novel technologies and the management of large amounts of data. Multiple technologies and sensors are already being used, or have the potential, to monitor important individual traits. These two interdisciplinary fields share multiple objectives that could lead to significant synergies. The complexity of in-farm data collection varies depending on the species and production system, with beef cattle presenting specific challenges. In addition, data collection needs to continue after slaughter, as carcass and meat quality traits are influenced by in vivo practices, determine the final profitability of the system, and need to be taken into consideration to modify management practices. Integrating livestock phenomics and precision livestock farming approaches will lead to a faster development of both fields and an optimal use of resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".