Update on the genus <i>Robertmurraya</i>: a bacterial genus honoring Dr. Robert G.E. Murray (with some personal reminiscences)
Bibliographic record
Abstract
The genus Robertmurraya was created by my group in 2020 to recognize the contributions of Dr. Robert G.E. Murray to the field of prokaryotic taxonomy. This manuscript updates the information regarding this genus. In addition to the seven Robertmurraya species with validly published names, the work presented here shows that two species with effectively published names, “ Bacillus yapensis” and “ Bacillus dakarensis”, and an uncharacterized Bacillus sp. Y1 are also affiliated with this genus. Based on these results, reclassification of “ Bacillus yapensis” as a novel species Robertmurraya yapensis sp. nov. is proposed. It is also suggested that “ Bacillus dakarensis”, for which strains are not available from culture collections, should also be recognized as “ Robertmurraya dakarensis”. This article also reflects on the serendipitous way I came to know Dr. Murray and his extensive interactions with me and strong support for our work for more than 10 years. Dr. Murray also introduced me and our work to his friend and contemporary Dr. Peter Sneath, who like him also contributed extensively to the field of prokaryotic taxonomy. This introduction led to a fruitful collaboration with Dr. Sneath leading to a joint publication describing the use of the Character Compatibility approach to molecular sequence data.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".