Study on Availability of Various Macro and Micro-Minerals in Lactating Buffaloes under Field Conditions of Sabarkantha District of Gujarat
Bibliographic record
Abstract
A study was conducted in the Sabarkantha district of Gujarat to assess the status of some macro and micro-minerals in lactating buffaloes. Feeds and fodder samples were collected from 17 representative villages of the district for analysis of macro and micro-minerals. Calcium content in cottonseed cake (0.17%), crushed maize (0.03%) and maize cake (0.22%) was found to be below the critical level (0.30%). The phosphorus content in concentrate ingredients was high (0.32-0.67%) but low in dry roughages (0.06-0.20%). Feeds and fodder were found to be adequate in magnesium (0.40%), sodium (0.29%) and potassium (1.15%). Straws were found to be deficient in sulphur (0.16%). Green roughages were good source of copper (12.31 ppm). Wheat straw was found to be low in zinc (19.71 ppm) but comparatively high in manganese (47.88 ppm) and iron (630.24 ppm). Lucerne and chikori green were found to be rich source of cobalt (>0.35 ppm). Selenium (0.68 ppm) was present in appreciable quantities in most of the feedstuffs. Lactating buffaloes were also found to be excess in energy and crude protein (70%), whereas, calcium and phosphorus were deficient in the ration (65%). Ration of lactating buffaloes was found to be deficient in Ca, P, S, Cu, Zn and Co. Supplementing the deficient minerals through area specific mineral mixture could alleviate the deficiency and improve productivity and reproduction efficiency of lactating buffaloes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".