Bibliographic record
Abstract
Drawing on documents both published and archival, this paper explains how the prokaryote-eukaryote dichotomy of the 1960s was constructed, the purposes it served, and what it implied in terms of classification and phylogeny. In doing so, I first show how the concept was attributed to Edouard Chatton and the context in which he introduced the terms. Following, I examine the context in which the terms were reintroduced into biology in 1962 by Roger Stanier and C. B. van Niel. I study the discourse over the subsequent decade to understand how the organizational dichotomy took on the form of a natural classification as the kingdom Monera or superkingdom Procaryotae. Stanier and van Niel admitted that, in regard to constructing a natural classification of bacteria, structural characteristics were no more useful than physiological properties. They repeatedly denied that bacterial phylogenetics was possible. I thus examine the great historical irony that the "prokaryote," in both its organizational and phylogenetic senses, was defined (negatively) on the basis of structure. Finally, we see how phylogenetic research based on 16S rRNA led by Carl Woese and his collaborators confronted the prokaryote concept while moving microbiology to the center of evolutionary biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".