How do metals interact with periphytic biofilms?
Bibliographic record
Abstract
Extracellular matrix of periphyton has complex structural and chemical composition regulating metal transfer within biofilms with consequences for metal transfer to aquatic food webs. We investigated which metal species were retained in the loosely (LB) and the tightly bound (TB) fractions of the periphyton matrix from three pristine lakes at different growth stages. We measured the fluorescent dissolved organic matter (FDOM) composition with parallel factor analysis (PARAFAC) and the co-occurrence of essential and non-essential metals with FDOM in the two matrix fractions. The LB and TB fractions of periphyton had distinct fluorescence composition from the water column. The PARAFAC model identified five components, including two (C2 and C4) appearing to be of periphytic origin. The humic-like C2 was almost exclusive to periphyton and the tryptophan-like C4, associated to biofilm phototrophy, represented up to 47.0 ± 7.3 % of total fluorescence in the LB fraction. Most metals had significant positive relationships with four FDOM components in the LB fraction while C2 was the only component in the TB fraction to show such significant relationships. Components in the LB fraction seemed to act as scavengers for metals, preventing them from reaching the cellular fraction, while C2 from the TB fraction was likely promoting the bioavailability of essential metals for microorganisms inside periphyton. This study highlights the contrasting roles of the extracellular matrix on metal mobility beyond the usually proposed protection mechanisms. We suggest an experimental model for the study of metal regulation processes of the periphytic extracellular polymeric substances with a focus on the components produced by microorganisms within periphyton and their distribution in the different matrix fractions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".