Bibliographic record
Abstract
The aim of this work is the identification and mapping of important points in designing gardens for dogs, especially for large breeds. Work includes the most likely and the most common canine habits that can irreversibly damage the garden as well as a way to prevent it. Work also includes how to properly establish a garden that the dog your space eg.: to play on branding, rest and raking. Especially dogs who are bred to dig and bolting prohibit that activity can not. However, we can establish a garden so that her dog while raking destroy and to set it to a special place. Of course the tiny breeds such as the "Čivava" or "Krysařík", can never cause harm, how easily inflict "Labrador" and "Německý ovčák". The workflow will consist of a research environment in which they would be placed garden. Otherwise, the proposed garden will look on a small or large plot in the city and in the country. It is also important to know the type of breed that will stay in the garden. Hunting breeds of dogs in the garden can do more damage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".