Estimation of Body Weight and Body Surface Area in Swamp Buffaloes using Visual Image Analysis
Bibliographic record
Abstract
The three dimensional computerized visual image analysis was performed to evaluate the body weight (BW) and body surface area (BSA) in swamp buffaloes. Nineteen swamp buffaloes were measured the conformation by linear measurement compared to 3D body scanner at different points : body height (A), heart girth (B), shoulder width (C), iliac width (D), ischial tuberosity width (E), the length between shoulder and ileac wing (F, G), the length between ileal wing to ischial tuberosity (H, I) and the length between shoulder to ischial tuberosity (J1, J2). The significant correlation was found between these two methods. The 3D body scanner was then performed in 28 males and 39 females for BW and 68 males and 74 non-pregnant and 31 pregnant females for BSA estimation. The appropriate models to estimate BW in buffaloes were BW = - 1174.07 + 4.31 (B) + 7.75 (FG) (R2 = 0.76, P<0.001), BW (male) = -1265.99 + 4.94(B) + 14.41(D) (R2 = 0.81; P<0.001) and BW (female) = -563.66 + 7.94 (C) + 14.77 (E) (R2 = 0.86; P<0.001). For BSA, the appropriate equations were BSA = -4.31 + 0.034(A) + 0.036 (J1J2) (R2 = 0.82, P<0.001), BSA (male) = -4.01 + 0.032 (A) + 0.037 (J1J2) (R2 = 0.816, P<0.001) and BSA (female) = -3.50 + 0.013(A) + 0.012 (B) + 0.040 (E) + 0.015 (J1J2) (R2 = 0.916, P<0.001). In conclusion, the 3D body scanner can be used to estimate BW and BSA in buffaloes with different models among males and females.
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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 | 0.000 |
| 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) | 0.002 | 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".