Leveraging aquatic-terrestrial interfaces to capture putative habitat generalists
Bibliographic record
Abstract
Habitat type is a strong determinant of microbial composition. Habitat interfaces, such as the boundary between aquatic and terrestrial systems, present unique combinations of abiotic factors for microorganisms to contend with. Aside from the spillover of certain harmful microorganisms from agricultural soils into water (e.g. fecal coliform bacteria), we know little about the extent of soil-water habitat switching across microbial taxa. In this study, we developed a proof-of-concept system to facilitate the capture of putatively generalist microorganisms that can colonize and persist in both soil and river water. We aimed to examine the phylogenetic breadth of putative habitat switchers and how this varies across different source environments. Microbial composition was primarily driven by recipient environment type, with the strongest phylogenetic signal seen at the order level for river water colonizers. We also identified more microorganisms colonizing river water when soil was collected from a habitat interface (i.e. soil at the side of an intermittently flooded river, compared to soil collected further from water sources), suggesting that environmental interfaces could be important reservoirs of microbial habitat generalists. Continued development of experimental systems that actively capture microorganisms that thrive in divergent habitats could serve as a powerful tool for identifying and assessing the ecological distribution of microbial generalists.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".