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Record W6887353075 · doi:10.15468/wvfqoi

International Barcode of Life project (iBOL) Barcode Index Numbers (BINs)

2016· dataset· en· W6887353075 on OpenAlexaboutno aff

Bibliographic record

VenueGlobal Biodiversity Information Facility · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBarcodeBiodiversityIndex (typography)Data collectionDNA barcoding

Abstract

fetched live from OpenAlex

Established in 2008, the International Barcode of Life Consortium (iBOL, http://www.ibol.org/) is a research alliance of nations with the desire to transform biodiversity science by building the DNA barcode reference libraries, the sequencing facilities, the informatics platforms, the analytical protocols, and the international collaboration required to inventory and assess biodiversity. iBOL has overseen the completion of one major program, BARCODE 500K, and a second program, BIOSCAN runs for seven years from June 2019. The first program barcoded 500,000 species reflecting the investment of $150 million by research organizations in 25 nations. Building on this success, BIOSCAN will extend barcode coverage to 2.5 million species by 2025. This program will stimulate activation of the Planetary Biodiversity Mission (PBM) – iBOL’s final project. PBM is a research initiative that will deliver a comprehensive understanding of the composition and distribution of multi-cellular life by 2045. iBOL maintains the Barcode of Life Data System (BOLD, http://www.boldsystems.org/). BOLD is a cloud-based data storage and analysis platform developed at the Centre for Biodiversity Genomics in Canada. It consists of four main modules, a data portal, an educational portal, a registry of BINs (putative species), and a data collection and analysis workbench.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.267

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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations3
Published2016
Admission routes1
Has abstractyes

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