Artificial substrata to assess ecological and ecotoxicological responses in river biofilms: Use and recommendations
Bibliographic record
Abstract
River biofilms are biological consortia of autotrophs and heterotrophs colonizing most solid surfaces in rivers. Biofilm composition and biomass differ according to the environmental conditions, having different characteristics between systems and even between river habitats. Artificial substrata (AS) are an alternative for in situ or laboratory experiments to handle the natural variability of biofilms. However, specific research goals may require decisions on colonization time or type of substrata. Substrata properties (i.e., texture, roughness, hydrophobicity) and the colonization period and site are selective factors of biofilm characteristics. Here we describe the uses of artificial substrata in the assessment of ecological and ecotoxicological responses and propose a decision tree for the best use of artificial substrata in river biofilm studies. We propose departing from the purpose of the study to define the necessity of obtaining a realistic biofilm community, from which it may be defined the colonization time, the colonization site, and the type of artificial substratum. Having a simple or mature biofilm community should guide our decisions on the colonization time and type of substrata to be selected for the best use of AS in biofilm studies. Tests involving contaminants should avoid adsorbing materials while those ecologically oriented may use any AS mimicking those substrata occurring in the streambed.•We review the utilization of different artificial substrata to colonize biofilm in river ecology and ecotoxicology.•We propose a decision tree to guide on selecting the appropriate artificial substrata and colonization site and duration.•Type of artificial substrata (material, size, shape...) and colonization duration are to be decided according to the specific purpose of the study.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".