Modeling Cadmium Exchange by an Aquatic Moss (<i>Fontinalis dalecarlica</i>)
Bibliographic record
Abstract
Although aquatic mosses are widely used as metal biomonitors in rivers, there are few effective models to describe metal uptake and loss by these plants. To fill this gap, we exposed the aquatic moss Fontinalis dalecarlica for 28 d to three Cd concentrations (approximately 5-50 nM) in a flow-through laboratory system. Cadmium accumulation by F. dalecarlica was rapid during the first few days of exposure and slowed thereafter but did not reach a steady state within our 1-month long experiment. This lack of a plateau in moss concentrations suggests that, for biomonitoring purposes, the duration of moss exposure should be considered either through a model of the type that we tested or by standardizing the exposure time of mosses transplanted in the field. During the subsequent 22-d elimination phase of our experiment, Cd concentrations in mosses did not return to their initial levels. This result suggests that a two-compartment model is likely to be more effective at describing Cd losses than would a one-compartment alternative. Indeed, predictions of a two-compartment model closely fitted our experimental data, which augurs well for the wider use of this model for other moss species and metals.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".