Interception frequency of exotic bark and ambrosia beetles (Coleoptera: Scolytinae) and relationship with establishment in New Zealand and worldwide
Bibliographic record
Abstract
Scolytinae species are among the most damaging forest pests, and many of them are invasive. Over 1500 Scolytinae interceptions were recorded at New Zealand's borders between 1950 and 2000. Among the 103 species were Dendroctonus ponderosae, Ips typographus, and other high-risk species, but actual arrivals probably included many more species. Interceptions were primarily associated with dunnage, casewood (crating), and sawn timber, and originated from 59 countries, mainly from Europe, Australasia, northern Asia, and North America. New Zealand and United States interception data were highly correlated, and 7 of the 10 most intercepted species were shared. Interception frequency and establishment in New Zealand were not clearly related. By combining New Zealand and United States interceptions of true bark beetles we obtained data on species found in shipments from around the world. Logistic regression analysis showed that frequently intercepted species were about four times as likely as rarely intercepted species to be established somewhere. Interception records of wood and bark borers are valuable for the prediction of invaders and for our general understanding of invasions. The use of alternatives to solid wood packaging, such as processed wood, should be encouraged to reduce the spread of invasive wood and bark borers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".