A novel method to assess effects of chemical stressors on natural biofilm structure and function
Bibliographic record
Abstract
Summary The regulation and management of chemical contaminants rarely use community‐ and ecosystem‐level endpoints, partly due to a lack of suitable methods. To overcome this limitation, we propose contaminant exposure substrata (CES), an adaptation of the widely used nutrient‐diffusing substratum method, to assess responses of biofilm communities to chemical contaminants in situ. We describe methods for using CES to assess effects on biofilm biomass, community structure, process rates, biofilm–consumer interactions and biofilm chemistry. We also provide equations to calculate the flux of soluble chemicals from CES and describe an approach to compare contaminant dose in CES assays to the contaminant dose encountered by biofilms in polluted surface waters. Data from four case studies demonstrate that CES can detect impairment of biofilm structure and function. The adaptability, simplicity and cost‐effectiveness of CES make them valuable tools to assess community‐ and ecosystem‐level responses to contaminants, suggesting potential for routine use and incorporation of data generated from such assays into contaminant regulation and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".