Positive and negative effects of allochthonous dissolved organic matter and inorganic nutrients on phytoplankton growth
Bibliographic record
Abstract
Dissolved organic matter (DOM) can have both positive and negative effects on phytoplankton growth. The magnitude of these effects may vary depending on the source of DOM and the composition of the phytoplankton community. Here, I address the relative importance of the positive and negative effects of DOM extracts on phytoplankton growth. In short-term experiments with phytoplankton from West Long Lake, a small, moderately coloured lake in northern Michigan, U.S.A., the net effect of doubling ambient DOM on phytoplankton growth was positive. Increasing DOM concentrations from ~10 mg C·L 1 to ~20 mg C·L 1 had a negative effect on total phytoplankton growth by reducing irradiance and thus reducing the depth to which growth was positive. However, inorganic nutrients in the DOM extracts increased growth at each irradiance level. The positive effect on phytoplankton growth owing to the nutrients associated with DOM was greater than the negative effect caused by shading. Although the positive effects of allochthonous DOM inputs outweighed the negative effects for the nutrient-limited phytoplankton in these experiments, the net effect depends on the concentration and availability of nutrients associated with allochthonous DOM as well as the physiological status of the phytoplankton community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".