Quantitatively evaluating mistaken clone assignments by RFLP analysis of 16S rRNA genes: a case study
Bibliographic record
Abstract
We quantitatively evaluated the errors of clone assignment based on the restriction fragment length polymorphism (RFLP) pattern of 16S rRNA genes. Eighty clones were randomly selected from a 16S rRNA gene library and were categorized into 35 operational taxonomic units (OTU) based on their indistinguishable enzyme restriction patterns of 3 tetrameric restriction enzymes RsaI, BsuRI, and HinfI. All of these clones were then sequenced and were reassigned into 36-53 OTUs using the DOTUR program when sequence similarities of 95%-100% were used. The number of the identically assigned clones ranged from 53 to 61 and the percentage varied from 66.3% to 76.3%. The Shannon-Weaver index for the bacterial community observed by RFLP analysis was 2.75, equal to that estimated by DOTUR at a 97% sequence similarity. Compared with clones assigned with the DOTUR program at a 97% sequence similarity, only 61 clones (76.3%) were correctly assigned by RFLP analysis. Six clones (7.5%) were assigned mistakenly at the phylum level, and the positions of 13 clones (16.2%) were phylogenetically different at a lower taxonomic rank.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".