Scientific abstracts from the 6th International Barcode of Life Conference / Résumés scientifiques du 6<sup>e</sup> congrès international « Barcode of Life »
Bibliographic record
Abstract
Background: Due to the overwhelming increase in multi-locus DNA barcode data provided by taxonomists and field scientists, sequence analysis techniques have been widely developed to effectively compare multi-locus sequences. On the one hand, traditional alignment-based methods are time-consuming and cannot be used for the analysis of non-alignable, multi-locus sequences. On the other hand, alignment-free algorithms allow for the establishment of similarity between biological sequences based on the counts of fixed-length substrings (k-mers) and have proved successful in many applications, including multi-locus DNA barcode analysis. Alignment-free algorithms rely on counting and comparing the frequency of all the distinct k-mers that occur in the considered sequences. Results: Here, we present LAF (Logic Alignment Free), a method that combines alignment-free techniques and rule-based classifiers in order to assign multi-locus DNA barcode sequences to their corresponding species. LAF looks for a minimal subset of k-mers whose relative frequencies are used to build the classification models as disjunctive-normal-form logic formulas (“if-then rules”, e.g., “if the frequency of AACT>0.03, then the species of the sequence is Mycena pura”). Significance: We successfully applied LAF to the classification of DNA barcode sequences belonging to the plant and fungus kingdoms. In particular, focusing our analysis on multi-locus barcode samples, we succeeded in obtaining reliable classification performances at different taxonomic levels by extracting a handful of rules.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.163 | 0.120 |
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".