Bibliographic record
Abstract
A common perception among dog owners is that their pets seem to anticipate the arrival of a member of the household. Surveys in Britain and the US have shown that between 45 and 52% of dog owners have noticed this kind of behaviour. People often ascribe this phenomenon to telepathy or a sixth sense but there may be more conventional explanations. The dog could be hearing or smelling its owner approaching, predicting the owner’s arrival based on a routine schedule or picking up on subtle cues from people at home who know when the absent person is returning. In order to control for these alternative hypotheses, a time-coded video camera will record the dog’s behaviour during the owner’s absence. The data will be analysed by someone who has no foreknowledge if and when the owner is arriving. The data will be divided into the pre-return (control) and return periods. The return period will begin once the owner departs for home. Other people in the house will have no idea what the owner is doing in order to control for subtle cues. To eliminate the possibility of hearing/smelling its owner, the dog should be capable of reacting at least 10 minutes in advance of the owner’s arrival. We will measure whether there is a statistically significant difference in anticipatory behaviour between the pre-return and return periods. If it is higher for the later, this experiment would support the hypothesis that dogs are sensitive to their owners intentions, even over long distances.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".