Dog owner's accuracy measuring different volumes of dry dog food using three different measuring devices
Bibliographic record
Abstract
Prior research demonstrates significant inaccuracy when repeatedly measuring the same amount of dry dog food using a dry-food measuring cup, bringing into question the accuracy of measuring devices. This study aimed to determine dog owners' accuracy when measuring different volumes of dry dog food using different types of measuring devices. One hundred dog owners, randomly assigned one of three measuring devices (a one-cup dry-food measuring cup, a two-cup graduated-liquid measuring cup or a two-cup commercial food scoop), were asked to measure ¼, ½ and 1 cup of dry dog food. Accuracy was assessed with an electronic gram scale by comparing measured volumes with the correct weight in grams. Individual accuracy ranged from -47.83% to 152.17% across devices and volumes. Measuring accuracy was found to be associated with the volume of food measured (p<0.001) and the type of measuring device used (p<0.001). Findings highlight approaches for decreasing excess intake of calories by dogs, including promotion of tactics to improve measurement accuracy (eg, gram scales, volume-calibrated dry-food measuring devices), especially for measuring small volumes.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".