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Integration of DNA barcoding into an ongoing inventory of complex tropical biodiversity

2009· article· en· W2054304199 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecular Ecology Resources · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingBiologyBarcodeBiodiversityLepidoptera genitaliaSpecies complexParasitoidEcologyZoologyMitochondrial DNATachinidaeHymenopteraPhylogenetic tree

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.241
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it