The responses of phytoplankton communities to elevated CO<sub>2</sub> show seasonal variations in the highly eutrophic Lake Taihu
Bibliographic record
Abstract
From April 2012 to January 2013 (over four seasons), in situ microcosm experiments were conducted in Lake Taihu, perturbed over a range of pCO 2 scenarios (270, 380, and 750 μatm; 1 atm = 101.325 kPa). The influence of CO 2 level on microcosms was greatest during the spring because of the high growth rate of phytoplankton. In this season, rising CO 2 levels caused a pH reduction, and the maximum reduction was 0.6 units when CO 2 level was enhanced from the present level to 750 μatm. The doubling of CO 2 level could increase the net primary production (NPP) by 65% during spring when the concentrations of other nutrients were maintained. The rise of NPP could cause a decline of dissolved inorganic carbon (DIC) concentration, and CO 2 enrichment might mitigate the extent of this decline. Meanwhile, higher CO 2 may slow or prevent a loss of diversity of phytoplankton in microcosms in this season. During the other three seasons, Microcystis predominated, and the percentage of cyanobacteria did not alter with the change of CO 2 . We did not observe a significant increase in the abundance of any taxa with the rise of CO 2 during the in situ microcosm experiments.
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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 | 0.000 | 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) | 0.001 | 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".