Diat.barcode, an open-access barcode library for diatoms
Bibliographic record
Abstract
Diatoms (Bacillariophyta) are ubiquitous microalgae which produce a siliceous exoskeleton and which make a major contribution to the productivity of oceans and freshwaters. They display a huge diversity, which makes them excellent ecological indicators of aquatic ecosystems, and can also be used to reconstruct paleoenvironments. Usually, diatoms are identified using characteristics of their exoskeleton morphology, which can be time consuming and error-prone. DNA-barcoding is an alternative to this and the use of High-Throughput-Sequencing enables the rapid analysis of many environmental samples at a lower cost than if specialist analysts are used. However, to identify environmental sequences correctly, an expertly curated reference library is needed. Several curated libraries for protists exists; none, however, are dedicated to diatoms. Diat.barcode is an open-access library dedicated to diatoms which has been maintained since 2012. It was initiated with the barcoding network of INRA (French National Institute for Agricultural Research) R-Syst, is now an international initiative partly supported by a Cost network (DNAqua-net). Data come from two sources (1) the NCBI nucleotide database (National Center for Biotechnology Information) and (2) unpublished sequencing data of culture collections in France, UK and Russia. Since 2017, several European experts have collaborated to curate this library for rbcL, a chloroplast marker suitable for species-level identification of diatoms. For the latests versions of the database, more than 8100 curated barcodes are available. The database is accessible through https://eng-carrtel-collection.hub.inrae.fr/barcoding-databases. A ready-to-use subset of the database for metabarcoding analyses is also accessible.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.063 |
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".