Population characteristics and neuter status of cats living in households in the United States
Bibliographic record
Abstract
OBJECTIVE: To gather data on cats living in US households, document their neuter status, and identify demographic characteristics associated with neuter status. DESIGN: Cross-sectional, random-digit-dial telephone survey. SAMPLE POPULATION: 1,205 adults in the continental United States contacted between April 24, 2007, and May 14, 2007. PROCEDURES: Information was gathered by means of computer-assisted telephone interviews. Multivariate logit analysis was used to identify demographic characteristics significantly associated with neuter status. RESULTS: 383 of 1,205 (31.8%) respondents reported having at least 1 cat at the time of the survey, yielding an estimated population of 82.4 million cats living in 36.8 million US households. Overall, 680 of 850 (80.0%) cats were reportedly neutered. Of the 371 neutered female cats, 303 (81.7%) had reportedly been neutered before having any litters. Proportion of cats that were neutered differed significantly across annual family income groups, with 96.2% (231/240) of cats in households with annual family incomes >or= $75,000 being neutered, 90.7% (231/254) of cats in households with annual family incomes between $35,000 and $74,999 being neutered, and only 51.4% (123/239) of cats in households with annual family incomes < $35,000 being neutered. CONCLUSIONS AND CLINICAL RELEVANCE: Findings suggested that a high percentage (80.0%) of cats living in households in the United States were neutered and that annual family income was the strongest predictor of whether cats in the household were neutered. The present study did not attempt to address stray and feral cats, which represent a substantial but unknown percentage of the total US cat population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".