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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; both teacher heads agree on what is shown here.
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".