INSECT REARING - A TOOL FOR DETECTING NON-INDIGENOUS WOOD BORING INSECTS
Bibliographic record
Abstract
Since 1998, the Canadian Food Inspection Agency (CFIA) has been using Lindgren funnel traps baited with the exotic bark beetle lure, ultra high release (UHR) ethanol, and UHR ethanol/UHR alpha-pinene to detect non-indigenous wood boring insects. However, several insects either do not respond to the lures used in this survey or do not rely on long-range chemicals to locate mates or host trees. Although effective at detecting some target species, this type of survey limits the spectrum of potentially detectable insects. Rearing insects from infested logs is a more generalized approach to detection because it does not exclude insects that do not respond to specific lures. As long as brood production of a given species occurs under the bark or within the wood of any tree, this survey has the capability to detect that insect. The CFIA, in partnership with the Canadian Forest Service, City of Surrey, City of Toronto, City of Montreal, Halifax Regional Municipality, and Vancouver Parks Board, is rearing infested logs as a tool for detecting established populations of non-indigenous wood boring insects. Steel marine transport containers (40 feet long) were modified into climate-controlled rearing facilities and placed in pre-selected locations in each of the cities (Surrey, Toronto, Montreal, Dartmouth). Logs that meet specific criteria (e.g., proximity to high risk sites, state of decline, signs of insect activity, etc.) are obtained through a city’s hazard tree removal program. Logs are placed in sleeve cages suspended from an overhead racking system or placed in modified sonotubes/building forms and held for insect emergence. To date, ambrosia beetles, weevils, bark beetles, longhorn beetles, and metallic wood borers have been reared from a variety of softwood and hardwood species. Although a few naturalized non-indigenous species have been collected, most of the reared insects are native. To date, there have not been any new records of introduced species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".