The status of taxonomy in Canada and the impact of DNA barcodingThe present review is one of a series of occasional review articles that have been invited by the Editors and will feature the broad range of disciplines and expertise represented in our Editorial Advisory Board.
Bibliographic record
Abstract
To assess the recent history of taxonomy in Canada and the impact of DNA barcoding upon the field, we performed a survey of various indicators of taxonomic research over the past 30 years and also assessed the current direct impact of funds made available for taxonomy through the DNA barcoding NSERC (Natural Sciences and Engineering Research Council of Canada) network grant. Based on results from surveys of three Canadian journals, we find that between 1980 and 2000 there was a 74% decline in the number of new species described and a 70% reduction in the number of revisionary studies published by researchers based in Canada, but there was no similar decline for non-Canadian-authored research in the same journals. Between 1991 and 2007 there was a 55% decline in the total amount of inflation-corrected funds spent upon taxonomic research by NSERC’s GSC18 (Grant Selection Committee 18); this was a result of both a decrease in the number of funded taxonomists and a decrease in mean grant size. Similarly, by 2000, the number of entomologists employed at the Canadian National Collection (CNC) had decreased to almost half their 1980 complement. There was also a significant reduction in the number of active arthropod taxonomists in universities across the country between 1989 and 1996. If these declines had continued unabated, it seems possible that taxonomy would have ceased to exist in Canada by the year 2020. While slight increases in personnel have occurred recently at the CNC, the decline in financial assistance for taxonomists has been largely reversed through funds associated with DNA barcoding. These moneys have increased the financial resources available for taxonomy overall to somewhere close to NSERC’s 1980 expenditures and have also substantially increased the number of HQP (highly qualified personnel) currently being trained in taxonomy. We conclude that the criticism “DNA barcoding has taken funds away from traditional approaches to taxonomy” is false and that, in Canada at least, the advent of DNA barcoding has reversed the dramatic decline in taxonomy. We provide recommendations on how to foster the future health of taxonomy in Canada.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".