Calibrating the taxonomy of a megadiverse family on BOLD: 2700 geometrid moth types barcoded (Geometridae, Lepidoptera)
Bibliographic record
Abstract
Background: One of the major challenges in creating a global database like BOLD is warranting the correct identification of the voucher specimens. The strict BOLD policy to require images, indication of specimen deposition, and accurate geo-referencing for all submitted datasets is extremely helpful to control doubtful data and potential misidentifications. Nevertheless, there are still many incomplete identifications (to genus or subfamily level), interim names, or even misidentifications on BOLD, mainly for species from tropical regions. Unfortunately, experts are lacking for many problematic groups and regions, and even when there are experts, they usually are not available for correcting the taxonomy of large amounts of data due to time constraints. Results: The best way to reliably calibrate the system is to barcode the original type specimens. In recent years, the challenge of sequencing up to 250-year-old museum specimens has been overcome by improved techniques and protocols developed by the Canadian Centre for DNA Barcoding. These innovations allowed for the generation of barcode sequences for ~2700 geometrid type specimens, which represent 2150 species corresponding to about 9% of the 23 000 described species worldwide. Significance: Here, we present case studies to show the efficiency, reliability, and sustainability of this approach as well as promising strategies to complete the calibration of the reference library within a reasonable amount of time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".