Survey of metabolically essential trace metals in inland lakes and reservoirs across Canada: What constitutes a low metal system?
Bibliographic record
Abstract
Trace metals are metabolically essential with many proteins dependent on metals for proper functioning yet little is known about the influence of low concentrations on freshwater microbial productivity and diversity. Dissolved iron (Fe), manganese (Mn), zinc (Zn), molybdenum (Mo), nickel (Ni), cobalt (Co), copper (Cu) and vanadium (V) were surveyed in 39 lakes and reservoirs across Canada representing different geology, dominant land uses, lake depth, trophic status and climatic zones. Concentrations varied considerably and cross-Canada patterns were not uniform among the eight metals, but PCA analysis revealed two major patterns: Co, Cu, Ni and V in one group and Fe and Mn in a second group. Sub-nanomolar concentrations of Co and Mo were common while sub-nanomolar concentrations of Zn, V and Ni were less common. Fe and Mn accumulated in the hypolimnion of the six lakes and reservoirs deep enough to thermally stratify with Co and Zn accumulation less common. Mo, Zn and Fe occasionally exceeded Canadian guidelines for protection of aquatic biota. Genomics and Monod growth kinetics were explored for their potential in identifying low metal environments and metal limitation without using metal enrichment bioassays. Metal concentrations in the cross-Canada survey were probably not low enough to limit growth but the impact of low metals on microbial diversity is unknown.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".