Porous underwater chamber (PUC) for in‐situ determination of nutrient and pollutant bioavailability to microorganisms
Bibliographic record
Abstract
Physical (e.g., temperature and light) and biogeochemical (e.g., cycling) parameters are known to affect bioavailability and toxicity of nutrients and pollutants to microorganisms. A system that would allow exposure of selected microorganisms to in situ conditions could provide relevant and novel evaluations of bioavailability. A simple and low cost 37 mL porous underwater chamber (PUC), in which test microorganisms are exposed to field conditions, is presented. The PUC is a thin, acrylic cylinder with polycarbonate membranes on each side, providing an optimal (75 cm2) exchange with the external solution. Regardless of the membrane pore size (0.4 and 5 µm), the PUC required 10 h to equilibrate with the external solution, close to the theoretical time determined for the diffusion of a model compound, the Suwannee River fulvic acid. For in situ use, the PUCs can be filled with filtered water to minimize the equilibration time. The system was validated in 20 to 60 L artificial freshwater by evaluating (1) Cd bioaccumulation by the microalga Chlamydomonas reinhardtii and (2) iron bioavailability to a cyanobacterial bioreporter in comparison with conventional bottle assays. The use of the PUC resulted in no significant differences in Cd uptake fluxes or iron bioavailability. Field experiments undertaken on Lake Erie demonstrated that the PUCs could be used to evaluate the contribution of particulate iron to iron bioavailability. Because the PUCs might not correctly reflect bioavailability for substances at low concentrations with high biouptake fluxes, a critical discussion with respect on the rate‐limiting steps of the device is presented. Several considerations that might facilitate the appropriate use of the PUCs are given.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".