PSIII-25 Dairy Data: Challenges and Opportunities.
Bibliographic record
Abstract
Data on dairy animals are being generated under a wide variety of conditions (laboratory, research facilities, on farm). Each year, hundreds of research trials are carried out on the genetics, metabolism, nutrition, physiology and behavior of dairy animals. Furthermore, companies and manufacturers are producing various devices and sensors capable of automatically collecting data on farms. The capture, integration, and use of the information generated represents both a great challenge but also a tremendous opportunity to allow the sector to progress more effectively. Datasets collected in the field of dairy science are growing rapidly, and are expected to increase exponentially in size in the near future. Numerous phenotypes (immune responses, behavioral observations, nutritional information, bodily fluids such as milk, blood, rumen fluid, etc.) are being recorded. Various cost-effective information-sensing mobile devices are being implemented or will be implemented in the near future, including remote sensing, software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. Finally, genomic information (SNP-chip genotypes, next-generation sequence data, metagenomic and epigenetic information) are also being collected on a number of animals. The volume and the diversity in the accuracy, format, scope, structure and pattern of this data are creating important challenges. Information is compiled and kept in idiosyncratic formats by different research groups or individual farmers. Finding and valorizing existing information is complicated. This leads to overlooking potentially valuable information. Here we present the development of a standardized and curated database, which enables higher-level, more comprehensive theoretical and experimental analyses. Our work is currently focused on dairy cattle, but will be expanded to include other livestock species in the near future.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.044 |
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".