A comparison of biofilms from macrophytes and rocks for taste and odour producers in the St. Lawrence River
Bibliographic record
Abstract
Given their widespread and prolific annual development in the St. Lawrence River (SLR), macrophytes (i.e. submerged aquatic plants) represent large surface areas for biofilm growth and potentially important sites for associated production of taste and odour (T&O) compounds. We therefore evaluated the importance of submerged macrophytes and their associated biofilms for production of T&O compounds, 2-methylisoborneol (MIB) and geosmin (GM), compared with biofilms from adjacent rocks. We also tested the hypothesis that production of these compounds would differ between macrophyte species, based on the premise that they are not inert substrates but directly influence the communities that colonise their surfaces. Samples collected from transects across the SLR between Kingston and Cornwall, ON were dominated by the flat-bladed Vallisneria spp., and the leafed Myriophyllum spicatum, Elodea canadensis, Chara spp., Potamgeton spp., and Ceratophyllum spp. Overall, MIB and GM levels in biofilms ranged widely between samples. Expressed per g dry weight of biofilm, median levels from macrophyte were 50 (range 1-5000) ng MIB g(-1) and 10 (<1 to 580) ng GM g(-1) compared with 50 (range 5-970) ng MIB g(-1) and 160 (1-1600) ng GM g(-1) from rocks. Based on non-parametric statistical analysis, levels of GM were higher on a g dry weight basis in biofilms from rocks than macrophytes (P = 0.02), but MIB levels were similar (P = 0.94). However, when normalised for differences in substrate surface area (i.e. ng cm(-2)), levels of both MIB and GM were higher in biofilms from rocks than from macrophytes (P < 0.01). There were no discernable differences in MIB and GM concentrations from biofilms of different macrophytes based on either g dry weight sample or surface area (P > 0.05). Overlying water (OLW) concentrations ranged between 2-45 ng L(-1) for MIB and 5-30 ng L(-1) for GM and were not correlated with levels in adjacent biofilms. However, OLW concentrations peaked in shallow, low energy embayments consistent with enhanced production and release of MIB and GM in nearshore areas. The results support our previous work showing the importance of biofilms on various surfaces (rocks, macrophytes and zebra mussels) for MIB and GM production in the SLR, but suggest that inert surfaces like rocks are more productive sites per unit surface area than macrophytes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".