Semiochemical-based integrated pest management of ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) in British Columbia’s forest industry: implemented in 1982 and still running
Bibliographic record
Abstract
Abstract Three species of ambrosia beetles (Trypodendron lineatum (Olivier), Gnathotrichus sulcatus (LeConte), and G. retusus (LeConte) (Coleoptera: Curculionidae: Scolytinae)) have been estimated to cause annual losses of $95–$189 million in degrade of logs and lumber on the British Columbia coast, in Canada. A consultant-run semiochemical-based integrated pest management programme was implemented in 1982 against these beetles, following fulfilment of four prerequisites: (1) presence of receptive potential clients, (2) availability of semiochemical lures, (3) invention of an operational trap, and (4) proof of concept of mass trapping technology. The programme is based on two broad strategies: maintain the problem at a tolerable level and, if necessary, reduce the problem to a tolerable level. One measure of effectiveness over 12 years of mass trapping at a dryland sort near Sooke, on Vancouver Island, British Columbia, was 16.4 million beetles trapped and an estimated five to one benefit-to-cost ratio. Despite success, several factors have conspired to reduce the programme from 50 sites serviced in early years to 7 in 2018. Timber companies in British Columbia are currently showing renewed interest and are taking steps to incorporate the integrated pest management programme as a formal component of their overall operations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".