A Step Toward Barcoding Life: A Model-Based, Decision-Theoretic Method to Assign Genes to Preexisting Species Groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A major part of the barcoding of life problem is assigning newly sequenced or sampled individuals to existing groups that are preidentified externally (by a taxonomist, for example). This problem involves evaluating the statistical evidence towards associating a sequence from a new individual with one group or another. The main concern of our current research is to perform this task in a fast and accurate manner. To accomplish this we have developed a model-based, decision-theoretic framework based on the coalescent theory. Under this framework, we utilized both distance and the posterior probability of a group, given the sequences from members of this group and the sequence from a newly sampled individual to assign this new individual. We believe that this approach makes efficient use of the available information in the data. Our preliminary results indicated that this approach is more accurate than using a simple measure of distance for assignment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it