Constitution of an international dataset on blood biomarkers in dairy cows: a preliminary study to develop milk MIR models
Bibliographic record
Abstract
Blood composition remains the gold standard to monitor and detect various health disorders of dairy cows. Estimating blood components through non-invasive methods would enable to scale up the measures in terms of cow number and time frequency, while aligning with societal requirements. This work begins with the constitution of a large dataset from multiple organizations across 12 countries. The first objective was to conduct an explanatory study, to better understand the variability of blood biomarkers regarding animal characteristics, sampling protocols and their relationship with other phenotypes of interest. The second objective was to improve on the large variability to develop robust models based on milk MIR spectra. Data merging resulted in a dataset of approximately 10,000 individual records of blood reference values and associated milk spectra. The majority of records were associated with blood BHB and NEFA, and fewer records with glucose, IGF-I, fructosamine, cholesterol, urea, progesterone, calcium and phosphorus. This preliminary work will facilitate a better understanding of the sources of variability in biomarkers, to highlight optimal modelling methodologies among linear and non-linear algorithms, and to estimate the capacities of milk MIR spectra to provide information on those traits under routine conditions,.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".