Integration of DNA barcoding into an ongoing inventory of complex tropical biodiversity
Bibliographic record
Abstract
Inventory of the caterpillars, their food plants and parasitoids began in 1978 for today's Area de Conservacion Guanacaste (ACG), in northwestern Costa Rica. This complex mosaic of 120 000 ha of conserved and regenerating dry, cloud and rain forest over 0-2000 m elevation contains at least 10 000 species of non-leaf-mining caterpillars used by more than 5000 species of parasitoids. Several hundred thousand specimens of ACG-reared adult Lepidoptera and parasitoids have been intensively and extensively studied morphologically by many taxonomists, including most of the co-authors. DNA barcoding - the use of a standardized short mitochondrial DNA sequence to identify specimens and flush out undisclosed species - was added to the taxonomic identification process in 2003. Barcoding has been found to be extremely accurate during the identification of about 100 000 specimens of about 3500 morphologically defined species of adult moths, butterflies, tachinid flies, and parasitoid wasps. Less than 1% of the species have such similar barcodes that a molecularly based taxonomic identification is impossible. No specimen with a full barcode was misidentified when its barcode was compared with the barcode library. Also as expected from early trials, barcoding a series from all morphologically defined species, and correlating the morphological, ecological and barcode traits, has revealed many hundreds of overlooked presumptive species. Many but not all of these cryptic species can now be distinguished by subtle morphological and/or ecological traits previously ascribed to 'variation' or thought to be insignificant for species-level recognition. Adding DNA barcoding to the inventory has substantially improved the quality and depth of the inventory, and greatly multiplied the number of situations requiring further taxonomic work for resolution.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".