Monod parameterization and competition at low iron among freshwater cyanobacteria and chlorophytes
Bibliographic record
Abstract
1. This study combines two approaches to explore the utility of Monod growth kinetics to predict competition outcomes between freshwater cyanobacteria and chlorophytes at low iron Fe. Fe threshold concentrations (FeT) below which growth ceases, and growth affinities (slope of Fe concentration vs growth rate near FeT) were estimated for three large-bodied cyanobacteria (two N-fixers and Microcystis) and two chlorophytes in batch cultures. 2. Mean FeT for N-replete cyanobacteria, N-deplete (when N-fixing) cyanobacteria and chlorophytes were 0.076, 0.736 and 0.245 nmol L-1 , respectively. Mean affinities were 0.937, 0.597 and 0.412 L nmol-1 d-1 , respectively. Assuming that the mean affinities are representative of their groups, affinities predict that N-replete cyanobacteria are more efficient at acquiring Fe than chlorophytes and should dominate when Fe is low but greater than their FeT. 3. A second study evaluated the competitive abilities of a pico-cyanobacterium and a third chlorophyte in dual species, serial dilution culture. The pico-cyanobacterium was dominant at 50 nmol L-1 total Fe (which limited both taxa) and 500 nmol L-1 total Fe. At 0.5 nmol L-1 total Fe, a stressful concentration below FeT during most of the incubation, growth rates and cell densities were extremely low but neither had washed out after several months. 4. These results show that Monod kinetics can successfully predict competition outcomes in laboratory settings at low Fe. While important, Monod kinetics are only one mechanism governing competition between cyanobacteria and eukaryotes in natural systems. Observed deviations from Monod predictions can be partially explained with known mechanisms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".