Economic estimates of invasive wild ungulate damage to livestock producers in Hawai'i
Bibliographic record
Abstract
BACKGROUND: Invasive ungulates (hoofed mammals), including deer, feral pigs, feral goats, and feral sheep, are known to cause damage to agriculture, property, natural resources, and many other commodities. Most of the information regarding the economic impacts of wild ungulates is from North America, where some of these species are native. To evaluate invasive ungulate damage to livestock producers in the Hawaiian Islands, which have no native ungulates, a survey was distributed to livestock producers across the state. RESULTS: Survey results described how total annual costs are distributed among damage, control, and repairs for survey respondents, who represented a significant percentage of total ranchland acreage across the islands. The estimates, excluding fixed fence installation, revealed an annual cost to livestock producers who responded to the survey of US$1.42 million, which ranged from $3.6 million to $7.5 million when extrapolated to the entire state. The large cost contributors included damage to property, pastureland repair, control costs (excluding fencing), supplemental feed, and predation of calves by wild pigs. Additionally, producers reported spending more than $2 million in upfront fence installation costs. Most of these costs were reported by respondents on the islands of Hawai'i and Moloka'i. CONCLUSION: Study results revealed substantial damage to state livestock producers due to wild ungulates and are useful in determining an invasive ungulate management strategy that can appropriately aid the most impacted sectors of Hawai'i. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".