Phytoplankton metabolite profiles from two Lake Ontario Areas of Concern reveal differences associated with taxonomic community composition
Bibliographic record
Abstract
Water quality and phytoplankton community composition are important factors that can indicate freshwater ecosystem health. We combined water quality, phytoplankton community, and metabolomic data from algae and water sampled from two embayments in Lake Ontario, Hamilton Harbour and the Bay of Quinte, over ten weeks from August to October in the year 2020. Metabolomics was performed using liquid chromatography tandem mass spectroscopy (LC-MS/MS) to identify changes in intracellular metabolites within algae communities over time, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) was used to characterize putative isomers of extracellular metabolites at sub-ppb mass accuracy. Results from this study indicate that Hamilton Harbour and the Bay of Quinte are two very different ecosystems with respect to water quality, phytoplankton metabolites, and phytoplankton community composition. Community composition is strongly driven by conductivity and nitrates in Hamilton Harbour, while the opposite is true in the Bay of Quinte. Metabolites including α-aminobutyric acid and glycine were found in larger abundance within algal communities at both locations, while taurine was more predominant in algal communities from the Bay of Quinte. These metabolic profiles could reflect the different communities of phytoplankton, and be alternative indicators of algal bloom growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".