Cyanobacterial diversity and activity in modern conical microbialites
Bibliographic record
Abstract
Modern conical microbialites are similar to some ancient conical stromatolites, but growth, behavior and diversity of cyanobacteria in modern conical microbialites remain poorly characterized. Here, we analyze the diversity of cyanobacterial 16S rRNA gene sequences in conical microbialites from 14 ponds fed by four thermal sources in Yellowstone National Park and compare cyanobacterial activity in the tips of cones and in the surrounding topographic lows (mats), respectively, by high-resolution mapping of labeled carbon. Cones and adjacent mats contain similar 16S rRNA gene sequences from genetically distinct clusters of filamentous, non-heterocystous cyanobacteria from Subsection III and unicellular cyanobacteria from Subsection I. These sequences vary among different ponds and between two sampling years, suggesting that coniform mats through time and space contain a number of cyanobacteria capable of vertical aggregation, filamentous cyanobacteria incapable of initiating cone formation and unicellular cyanobacteria. Unicellular cyanobacteria are more diverse in topographic lows, where some of these organisms respond to nutrient pulses more rapidly than thin filamentous cyanobacteria. The densest active cyanobacteria are found below the upper 50 μm of the cone tip, whereas cyanobacterial cells in mats are less dense, and are more commonly degraded or encrusted by silica. These spatial differences in cellular activity and density within macroscopic coniform mats imply a strong role for diffusion limitation in the development and the persistence of the conical shape. Similar mechanisms may have controlled the growth, morphology and persistence of small coniform stromatolites in shallow, quiet environments throughout geologic history.
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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 | 0.000 | 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) | 0.000 | 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".