International Barcode of Life project (iBOL)
Bibliographic record
Abstract
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.046 |
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".