A comparison of bird‐feeding practices in the United States and Canada
Bibliographic record
Abstract
ABSTRACT Millions of Americans and Canadians participate in the feeding of wild birds. We surveyed hobbyists about their bird‐feeding experience, and examined demographic and regional differences in responses, to determine the types of bird‐feeding practices taking place and to identify themes important for wildlife managers to communicate with people who feed birds. Between autumn 2005 and winter 2008–2009, we recruited a non‐random sample from the interested public though both print and electronic media. We had 1,291 individuals from 48 states (USA) and 7 Canadian provinces who completed our mail and website survey. Survey respondents were primarily female (67%) and ≥45 years old (77%). Most respondents offered alternative foods in addition to traditional bird seed (≥82%) and provided other resources besides food to attract birds (≥75%). Our respondents fed birds because it brought nature (84%) and accompanying sound (81%) to the area, as a hobby (79%), and to help the birds (79%). Respondents felt attracting more bird species (69%), a greater number of birds (41%), and no pests (35%) would make their bird‐feeding experience more satisfying. Given the interested public's desire to increase bird diversity at their feeders and to help birds, managers have the opportunity to develop messages promoting habitat enhancement in addition to feeding, and provide suggestions for reducing the risk of disease transmission and pest species at feeders. © 2013 The Wildlife Society.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".