499 Late-Breaking: Impact of a High Calcium Diet in Growing Labrador Retriever Puppies
Bibliographic record
Abstract
Abstract An important function of feeding large breed puppies is providing appropriate amounts of calcium and phosphorus, which is used in skeletal mineralization during growth. National Research Council stated calcium requirements are 0.8–1.2%, and phosphorus 1.0–1.6%. The objective of this study was to compare a high calcium diet (Ca: 5.7%; P: 2.9%) (Nature’s Logic Canine Beef Meal Feast; Nature’s Logic) (HC) to a normal control diet (Ca: 1.6%; P: 1%) (Purina Puppy Chow; Nestle Purina) (CON) in growing Labrador Retriever puppies. Thirty-two puppies (16 HC/16 CON) were used in two 10wk modified AAFCO large breed puppy growth trials. Body weights were measured weekly, feed intake daily, and digestibility, body composition, hematology, chemistry, and bone metabolism biomarkers were measured at 8wks, 13wks, and 18wks of age. All puppies passed all AAFCO large breed puppy growth requirements, including veterinary exams, body weight gain, and bloodwork parameters. Both groups had similar average weight gains from baseline to the end of the trial. No significant differences in bone mineral density were found between HC and CON groups at any timepoint. No significant differences were found between groups after baseline for parathyroid hormone, calcitonin, or tartrate resistant acid phosphatase bone metabolism biomarkers. Total tissue mass, fat mass, and lean mass were lower in the HC group compared to CON group (P < 0.05), likely due to lower caloric content and metabolizable energy in the HC diet than expected. Calcium digestibility was significantly higher in HC diet vs CON diet (P < 0.01). Based on this data, a high calcium diet had no negative impact on major physiological parameters in growing Labrador Retriever puppies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".