The effect of copper and manganese on phytoplankton in the Grand River (southern Ontario), Lake Erie and Pacific Ocean ecosystems
Bibliographic record
Abstract
With the increased use and loading of metals into the environment, the accumulation of toxic metals by phytoplankton has become a concern. Trace metal interactions with phytoplankton are of particular interest due to the influence of phytoplankton on the biogeochemical cycling of metals in aquatic systems. The study of the accumulation of metals and their toxicity in phytoplantkon is also of interest since phytoplankton lie at the base of many aquatic food webs. Toxic metals therefore have the potential to disrupt food webs and may have important implications on aquatic ecosystems. This study has chosen to focus on the response of phytoplankton to two trace metals in particular: copper (Cu) and manganese (Mn). Although both Cu and Mn are essential elements for phytoplankton, Cu is of particular interest as a toxicant. A number of laboratory studies have suggested that there exists a physiological interaction between Cu and Mn, and that Cu toxicity can be decreased in the presence of high concentrations of Mn. However, few studies have examined the effects of these metals on phytoplankton in their natural environments. The significance of this study is that it is one of the first to examine whether the importance of Cu toxicity and the interaction between Cu and Mn observed in the laboratory is also observable under natural conditions. Short-term bioassays were conducted in order to observe the response of phytoplankton from the Grand River (Southern Ontario) and Lake Erie to additions of various concentrations of eu and Mn under natural conditions. Similar long-term bioassay experiments were also conducted in the Pacific Ocean. Experiments in the Grand River and the Pacific Ocean revealed no significant decrease in phytoplankton biomass or in photosynthetic efficiency with the addition of various concentrations of Cu and Mn. In Lake Erie, phytoplankton biomass was only adversely affected following relatively high additions of Cu of 60 nM, and only under certain conditions. These results seem to indicate that under the tested conditions, Cu toxicity may not be of particular concern to the phytoplankton of the Grand River, Lake Erie and Pacific Ocean ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".