Adapting Macroecology to Microbiology: Using Occupancy Modeling To Assess Functional Profiles across Metagenomes
Bibliographic record
Abstract
Metagenomics is maturing rapidly as a field but is hampered by a lack of available statistical tools. A primary area of uncertainty is around missing genes or functions from a metagenomic data set. Here, we borrow an established modeling approach from macroecology and adapt it to metagenomic data sets. Rather than multiple sampling trips to a specific area to detect a species of interest (e.g., identifying a cardinal in a forest), we leverage the enormous amount of information within a metagenome and use multiple gene markers for a function of interest (e.g., subunits of an enzyme complex). We applied our adapted occupancy modeling to a case study examining methane cycling capacity. Our models show methanogens and methanotrophs are both more likely to cooccur than be present in the absence of the other guild. The lack of consistent and complete metadata is a significant hurdle for increasing the statistical rigor of metagenomic analyses.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".