Fenced community gardens effectively mitigate the negative impacts of white-tailed deer on household food security
Bibliographic record
Abstract
White-tailed deer (Odocoileus virginianus) are large herbivores that thrive in urban and peri-urban landscapes. Their voracious appetite and ubiquity have made deer a significant threat to growing food in home and community gardens; features that often make important contributions towards household food security. Focusing on food availability, stability, utilization, and access, I outline how white-tailed deer threaten household food security. Deer threaten availability of food by widely consuming plants grown for human consumption. Deer threaten stability of household food security by causing spatially and temporally unpredictable food losses. Deer threaten utilization of food, through acting as sources of food-borne pathogens (i.e. Escherichia coli O157:S7). Deer threaten access to food by necessitating relatively high-cost economic interventions to protect plants from browsing. Although numerous products are commercially available to deter deer via behavioural modification induced by olfaction and sound – evidence of efficacy is mixed. Physical barriers can be highly effective for reducing deer browsing, but often come with a high economic cost. Users of community gardens benefit from fencing by receiving shared protection against deer herbivory at a significantly lower per capita cost. Among many other benefits, fenced community gardens are useful in mitigating the threats of white-tailed deer to household food security.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".