Bacterial response to dissolved organic matter affects resource availability for algae
Bibliographic record
Abstract
In aquatic systems, the presence of colored dissolved organic matter (DOM) may affect algal growth in numerous ways. This paper focuses on the effects of DOM on resource availability. DOM contains nitrogen and phosphorus, which may become available following microbial or photochemical degradation. Also, addition of DOM may stimulate bacterial growth, which in turn may change the availability of nitrogen, phosphorus, and inorganic carbon to algae. Experiments conducted in a moderately colored lake showed that the effect of DOM on algal growth depended on the amount of nutrients present in the peat extract and on bacterial response to DOM. There was evidence for competition for phosphorus between algae and bacteria in some treatments. In addition, when both bacteria growth and algal growth were high, bacterial respiration of DOM alleviated algal carbon limitation by providing algae with an inorganic carbon source. Thus, the degree to which bacteria are stimulated by the addition of DOM will affect the amount of phosphorus and inorganic carbon available for algal growth. These results suggest that part of the difficulty in predicting algal response to changes in DOM and nutrient concentration may be due partially to variability in bacterial responses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".