Which is more important for classifying microbial communities: who’s there or what they can do?
Bibliographic record
Abstract
Classification is a machine-learning approach to develop predictive models that can classify samples into categories correctly. In microbial studies, these categories include disease states and habitats. An ongoing question in microbial ecology is the correct level of analysis to use in order to best discriminate biologically relevant samples. Many studies use the 16S rRNA gene as a taxonomic marker, and then ask how effectively the taxonomic profiles obtained from this marker classify or cluster different microbial communities according to their sample types. Interestingly, the answer may depend on the question being asked. For phylogenetic analysis, different levels of resolution in grouping are differentially successful at different classification tasks. These classification tasks include separating different samples by the person they came from (which depends on fine distinctions among very closely related strains or species), and separating lean from obese individuals (where very broad groups of taxa are more effective) ( Knights et al., 2011b ).
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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".