Joining Inventory by Parataxonomists with DNA Barcoding of a Large Complex Tropical Conserved Wildland in Northwestern Costa Rica
Bibliographic record
Abstract
BACKGROUND: The many components of conservation through biodiversity development of a large complex tropical wildland, Area de Conservacion Guanacaste (ACG), thrive on knowing what is its biodiversity and natural history. For 32 years a growing team of Costa Rican parataxonomists has conducted biodiversity inventory of ACG caterpillars, their food plants, and their parasitoids. In 2003, DNA barcoding was added to the inventory process. METHODOLOGY/PRINCIPAL FINDINGS: We describe some of the salient consequences for the parataxonomists of barcoding becoming part of a field biodiversity inventory process that has centuries of tradition. From the barcoding results, the parataxonomists, as well as other downstream users, gain a more fine-scale and greater understanding of the specimens they find, rear, photograph, database and deliver. The parataxonomists also need to adjust to collecting more specimens of what appear to be the "same species"--cryptic species that cannot be distinguished by eye or even food plant alone--while having to work with the name changes and taxonomic uncertainty that comes with discovering that what looked like one species may be many. CONCLUSIONS/SIGNIFICANCE: These career parataxonomists, despite their lack of formal higher education, have proven very capable of absorbing and working around the additional complexity and requirements for accuracy and detail that are generated by adding barcoding to the field base of the ACG inventory. In the process, they have also gained a greater understanding of the fine details of phylogeny, relatedness, evolution, and species-packing in their own tropical complex ecosytems. There is no reason to view DNA barcoding as incompatible in any way with tropical biodiversity inventory as conducted by parataxonomists. Their year-round on-site inventory effort lends itself well to the sampling patterns and sample sizes needed to build a thorough barcode library. Furthermore, the biological understanding that comes with barcoding increases the scientific penetrance of biodiversity information, DNA understanding, evolution, and ecology into the communities in which the parataxonomists and their families are resident.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".