The importance of metagenomic surveys to microbial ecology: or why Darwin would have been a metagenomic scientist
Bibliographic record
Abstract
Scientific discovery is incremental. The Merriam-Webster definition of 'Scientific Method' is "principles and procedures for the systematic pursuit of knowledge involving the recognition and formulation of a problem, the collection of data through observation and experiment, and the formulation and testing of hypotheses". Scientists are taught to be excellent observers, as observations create questions, which in turn generate hypotheses. After centuries of science we tend to assume that we have enough observations to drive science, and enable the small steps and giant leaps which lead to theories and subsequent testable hypotheses. One excellent example of this is Charles Darwin's Voyage of the Beagle, which was essentially an opportunistic survey of biodiversity. Today, obtaining funding for even small-scale surveys of life on Earth is difficult; but few argue the importance of the theory that was generated by Darwin from his observations made during this epic journey. However, these observations, even combined with the parallel work of Alfred Russell Wallace at around the same time have still not generated an indisputable 'law of biology'. The fact that evolution remains a 'theory', at least to the general public, suggests that surveys for new data need to be taken to a new level.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.038 |
| Scholarly communication | 0.005 | 0.021 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.014 |
| 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".