A comparison of different pre and post-slaughter measurements for estimating fat reserves in Spanish <i>Blanca Celtibérica</i> goats
Bibliographic record
Abstract
Five pre-slaughter measurement values and four post-slaughter measurement values were used to estimate the weight of fat in the omental (OM), mesenteric (MES), perirenal (PR), subcutaneous (SC), and intermuscular (IM) fat depots in goats. The pre-slaughter measurements were: live weight (LW), sternal body condition score (BCSs), lumbar body condition score (BCSl), sternal fat thickness (FTs) and lumbar fat thickness (FTl) measured by ultrasound. The post-slaughter measurements were empty live weight (ELW), hot carcass weight (HCW), adipocyte diameter in the sternal subcutaneous fat (ADSCs) and adipocyte diameter in the lumbar subcutaneous fat (ADSCl). Linear and multiple regressions were fit to data collected from 22 adult, non-pregnant and non-lactating Blanca Celtibérica does. The results obtained showed BCSs, ranging from 1.5 to 4.5 (scale: 0-5) to be the best pre-slaughter estimator of an animal's total fat (R2 = 0.90, RSD = 2.252 kg) and HCW to be the best post-slaughter estimator (R2 = 0.92, RSD = 1.972 kg). Additionally, multiple regression using HCW and ADSCl together yielded estimates of the total amount of fat in all five of the depots considered here with an R2 = 0.95 and an RSD = 1.542 kg. Therefore, the use in vivo of BCSs is the best method for predicting nutritional status in does in extensive production systems in the Mediterranean region.Key words: Fat reserve, body weight, carcass weight, body condition score, ultrasound, adipocyte, goat
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 | 0.001 | 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) | 0.000 | 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".