Illuminating phylogeny and function of microbial dark matter in Sakinaw Lake
Bibliographic record
Abstract
Microorganisms are the most abundant and diverse forms of life on Earth. Interconnected microbial communities drive matter and energy transformations integral to ecosystem functions and services through distributed metabolic networks innovated over 3.5 billion years of evolution. To effectively harness this metabolic potential it is necessary to chart uncultivated microbial community structure and function. Cultivation-independent studies indicate that over half of the microbial diversity on Earth belongs to uncultivated candidate divisions, also known as microbial dark matter (MDM). Here, I illuminate MDM structure and function in meromictic Sakinaw Lake on the Sunshine Coast of British Columbia Canada. Sakinaw Lake water column conditions quantified over 8 field campaigns were intimately associated with unprecedented abundance and diversity of MDM. Using network analysis and single cell genomics, co-occurrence patterns between MDM including OP9/JS-1, OP8 and methanogenic Archaea were linked with potential to perform syntrophic acetate oxidation, an important process in anaerobic digestion of organic matter in natural and engineered ecosystems. Single-cell and metagenome analysis revealed previously unrecognized nitrate reduction potential in candidate division OP3 and uncovered a novel archaeal lineage. In addition, numerous Fe-S oxidoreductases associated with MDM in Sakinaw Lake indicate the potential to couple sulfur oxidation to iron reduction. Taken together, my work establishes Sakinaw Lake as a natural laboratory in which to explore MDM structure and function, shines a spotlight on known and novel interactions and metabolic capabilities among these most enigmatic microorganisms, and points to potential biotechnological innovations based on cooperative interactions between MDM populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".