Bibliographic record
Abstract
The economic, social and biological value of our forests makes their sustainability essential to our well-being. To ensure their long-term health, it is critical to regularly and effectively monitor their inhabitants, as well as to detect non-indigenous species early and accurately. These programs rely on the precise diagnosis of species, which can be complicated for terrestrial arthropods by sizeable trap samples, damaged specimens, immature life stages and incomplete taxonomy. The recent advent of DNA barcoding, a technique that differentiates species using sequence variation in a standard gene region, shows tremendous promise for circumventing these obstacles. This dissertation evaluates the integration of barcoding into forest arthropod biomonitoring and biosurveillance programs with several investigations of nocturnal moths (Lepidoptera) in British Columbia, Canada. Barcode reference libraries are constructed for looper moths (Geometridae) and Lymantria (Erebidae) tussock moths, and are determined to successfully discriminate species in over 93% and 97% of cases, respectively. The libraries demonstrate how barcoding might enhance biosurveillance programs by flagging two new records for geometrid moths, and by successfully diagnosing 32 intercepted tussock moth specimens. These two libraries, and a multi-gene phylogeny constructed for Geometridae, are used to conduct faunal inventories in modified forest systems, and investigate the influence of disturbance on three levels of moth diversity—species, genetic, and phylogenetic. A first level inventory of Stanley Park, Vancouver, produces a preliminary list of 190 species, the detection of four new exotic species, and the discovery of two potentially cryptic species. Surveys conducted across several harvest treatments at two silvicultural research forests display no evidence of increased diversity at intermediate disturbance levels, but do reveal a correlation between species and genetic diversity. And lastly, three levels of moth diversity are estimated in ponderosa pine systems that differ widely in attack by Dendroctonus bark beetles, and demonstrate a negative association between species diversity and tree mortality. In combination, all projects suggest that DNA barcoding provides several advantages over traditional biosurveillance and biomonitoring, including the ability to rapidly sort specimens, a reduction in specialist time, the detection of species at low density, and the ability to appraise multiple levels of diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".