Citizen monitoring of invasive species: wing morphometry as a tool for detection of alien <i>Tetropium</i> species
Bibliographic record
Abstract
Abstract The increasing threat of alien wood‐boring insect has resulted in the initiation of large‐scale monitoring programmes. These programmes are most often based on pheromone‐bailed traps, which allow the early detection and monitoring of invasive species. This approach is expensive because it entails the processing and accurate identification of large numbers of specimens. One of the most often suggested solutions to this problem is citizen participation in the monitoring of invasive species. Such an approach has the potential for reducing costs as well as providing data from a larger number of sites. However, citizens vary in taxonomic expertise and experience which can result in identification errors. This may be particularly important in the case of wood borers which include many morphologically similar species. In this study, we develop and discuss a semi‐automated method of identifying four morphologically similar and invasive Tetropium spp. wood borers as a potential tool for citizen‐based monitoring programmes. Identification is based on wing measurements and requires neither specialist knowledge nor expensive equipment. The method correctly identified the species of Tetropium with an error ranging from 1.3% for T. fuscum to 7.5% for T. cinnamopterum. We found that experience level of the individual user was not essential for correct identification; on average, inexperienced volunteers correctly identified the Tetropium species in 93% of cases. Further development of this method may be a significant step to overcoming the taxonomical impediment to citizen monitoring of taxonomically challenging groups of insects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".