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Record W6931303030 · doi:10.5281/zenodo.3788276

Atteva zebra Duckworth 1967

2010· article· en· W6931303030 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingBiodiversityZEBRA (computer)Tree (set theory)Key (lock)

Abstract

fetched live from OpenAlex

Atteva zebra Duckworth Atteva zebra Duckworth, 1967: 71. Type: Barro Colorado Island, Canal Zone, PANA- MA (WD and SS Duckworth collectors, 9 th May 1964) (USNM). Forewings. The zebra pattern makes this species readily distinct from the other species presented here (Figure 3D). Habitat and food plants. A. zebra is the common webworm of shoot tips of Simarouba amara saplings and adult trees in ACG rain forest (n = 123) It is more abundant than A. pustulella, but may be found on the same individual tree with A. pustulella and an occasional A. aurea in anthropogenic rain forest habitats. It has never been found on S. glauca or in ACG dry forest. Distribution. Known only from Costa Rica and Panama. Concluding remarks This case study demonstrates the value of combining morphological, ecological and DNA barcode information when working with similar species. Atteva is an example where seemingly confusing morphological and ecological patterns, can be definitively partitioned in the light of discrete data such as DNA sequences. The integration and synthesis of inventories, each one necessarily regionally focused, is facilitated by DNA barcodes, an efficiently communicated online character system. This was demonstrated by the fact that taxonomic problems surrounding the ailanthus webworm moth persisted in the ACG for 25 years and surfaced only recently. From the starting point of DNA barcode analyses it has been relatively straightforward to reach a taxonomic conclusion by joining taxonomic knowledge in the form of the name-bearing types with ecological and morphological information. The purported difficultly in obtaining barcodes from type material has been viewed as an obstacle to the melding of DNA barcoding information with other taxonomic information. Recent studies (Hausmann et al. 2009), including this one, show that this is not necessarily the case.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.006

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 head0.229
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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