Experimental assessment of the sensitivity of an estuarine phytoplankton fall bloom to acidification and warming
Bibliographic record
Abstract
We investigated the combined effect of ocean acidification and warming on the dynamics of the phytoplankton fall bloom in the Lower St. Lawrence Estuary (LSLE), Canada. Twelve 2600 L mesocosms were set to initially cover a wide range of pH T (pH on the total proton scale) from 8.0 to 7.2 corresponding to a range of p CO 2 from 440 to 2900 µatm, and two temperatures (in situ and +5 ∘ C). The 13-day experiment captured the development and decline of a nanophytoplankton bloom dominated by the chain-forming diatom Skeletonema costatum . During the development phase of the bloom, increasing p CO 2 influenced neither the magnitude nor the net growth rate of the nanophytoplankton bloom, whereas increasing the temperature by 5 ∘ C stimulated the chlorophyll a (Chl a ) growth rate and maximal particulate primary production ( P P ) by 76 % and 63 %, respectively. During the declining phase of the bloom, warming accelerated the loss of diatom cells, paralleled by a gradual decrease in the abundance of photosynthetic picoeukaryotes and a bloom of picocyanobacteria. Increasing p CO 2 and warming did not influence the abundance of picoeukaryotes, while picocyanobacteria abundance was reduced by the increase in p CO 2 when combined with warming in the latter phase of the experiment. Over the full duration of the experiment, the time-integrated net primary production was not significantly affected by the p CO 2 treatments or warming. Overall, our results suggest that warming, rather than acidification, is more likely to alter phytoplankton autumnal bloom development in the LSLE in the decades to come. Future studies examining a broader gradient of temperatures should be conducted over a larger seasonal window in order to better constrain the potential effect of warming on the development of blooms in the LSLE and its impact on the fate of primary production.
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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 | 0.001 |
| 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".