Inter-strain diversity among coastal picocyanobacteria across salinity gradients
Bibliographic record
Abstract
Abstract Strains of the picocyanobacteria genera Synechococcus and Cyanobium occur in cold-temperate zones from coastal waters to the open ocean, spanning a range of salinities and water depths. To thrive across different salinities, picocyanobacteria strains acclimate internal turgor by expending metabolic energy to accumulate compatible solutes and extrude inorganic ions. We grew five picocyanobacterial strains, derived from a range of source habitat salinities, under a matrix of light and salinity levels. Although Chl is a general proxy for phytoplankton biomass, Chl/cell is itself a key component of acclimation. Indeed, for all strains, except the brackish water CZS48M, the conditions supporting the highest Biovolume-specific growth rates shifted upwards towards higher salinities and PAR, compared to conditions for maximum Chl-specific growth. Therefore, the apparent optimal niche for a strain varies depending upon the metric used to track growth. Strains CZS48M and CZS25K derived from brackish lagoons of the Baltic Sea may be true ‘brackobionts’, with maximal growth at brackish salinities and high PAR, coinciding with their maximal metabolic capacities. CZS48M and CZS25K both also show high metabolism under higher salinity and low PAR, suggesting a situation of rapid metabolism, but lower achieved growth, under stress at the edges of their environmental tolerance ranges. Conversely, NIES981 (full marine, East China Sea) shows rapid metabolism under high, but also under low, salinities. The preferred salinity of each strain is consistent with its genome-encoded capabilities to synthesize different compatible solutes. In general, across strains, maximal metabolic rate is often offset from growth rate optima, showing conversion of metabolic electrons to biomass varies widely depending upon strain and condition, likely partly reflecting the costs of turgor regulation at the limits of salinity tolerance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".