iBarcode.org: web-based molecular biodiversity analysis
Bibliographic record
Abstract
BACKGROUND: DNA sequences have become a primary source of information in biodiversity analysis. For example, short standardized species-specific genomic regions, DNA barcodes, are being used as a global standard for species identification and biodiversity studies. Most DNA barcodes are being generated by laboratories that have an expertise in DNA sequencing but not in bioinformatics data analysis. Therefore, we have developed a web-based suite of tools to help the DNA barcode researchers analyze their vast datasets. RESULTS: Our web-based tools, available at http://www.ibarcode.org, allow the user to manage their barcode datasets, cull out non-unique sequences, identify haplotypes within a species, and examine the within- to between-species divergences. In addition, we provide a number of phylogenetics tools that will allow the user to manipulate phylogenetic trees generated by other popular programs. CONCLUSION: The use of a web-based portal for barcode analysis is convenient, especially since the WWW is inherently platform-neutral. Indeed, we have even taken care to ensure that our website is usable from handheld devices such as PDAs and smartphones. Although the current set of tools available at iBarcode.org were developed to meet our own analytic needs, we hope that feedback from users will spark the development of future tools. We also welcome user-built modules that can be incorporated into the iBarcode framework.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.051 |
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".