Extremely thermophilic endospores germinate and metabolise organic carbon in sediments heated to above 80°C
Bibliographic record
Abstract
Abstract Endospores of thermophilic bacteria are widespread in cold seabed environments where they remain dormant during initial burial in accumulating sediments. The temperature increase during sedimentation can be simulated in experimental heating of sediments, resulting in the temperature-dependent activation of different endospore populations from the microbial seed bank. Here we investigated the response of endospore populations to heating at extreme high temperature (80– 99°C). Metabolites for germination and organic matter degradation (dipicolinic acid and organic acids) revealed both endospore germination and subsequent metabolism at ≥80°C. Endospore-forming Firmicutes with the genomic potential for organic carbon and nitrogen transformation were recovered by genome-resolved metagenomics. Genomes from Symbiobacteriales, Thermosediminibacteriales, Moorellales and Calditerricolales encode multiple mechanisms for high temperature degradation of sedimentary organic carbon and features of necromass that accumulate during sediment burial including saccharides, amino and nucleic acids. The results provide insight into the metabolism of novel carbon cycling microorganisms activated at high temperature, and suggest that extremely thermophilic Firmicutes dispersed in the ocean are poised to germinate in response to sediment heating during burial and transform a wide range of organic substrates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".