Plant sampling uncertainty: a critical review based on moss studies
Bibliographic record
Abstract
Estimates of the distribution, migration, and accumulation of trace elements using mosses as bioindicators have successfully been used in biomonitoring studies since at least the 1970s. Chemical analysis of moss samples is also an important tool for assessing concentrations of elements in analyzed material at a given time. To achieve satisfactory accuracy in environmental studies, the best sampling approach must be used. Methods for the estimation of uncertainty derived from analytical procedures are well recognized, but the errors generated as a result of sampling are very often overlooked. Sampling uncertainty can be managed by a judicious selection of the sampling method, the amount of samples collected, and by following appropriate type of sampling protocols. The sampling protocol generally contains information about location of sampling sites, time of sampling (e.g., season), the species collected, type of sample (single, sub-sample), and monitored parameters (e.g., climate, analyzed substances). Information about seasonal variability; topographic, climatic, edaphic, and hydrologic conditions (type and amount of precipitation, rosewind); age; and part of plant that was collected is often ignored. There is no precise information on how these factors affect the sampling step and overall uncertainty over what procedures must be followed to reduce errors derived from plant sampling. This information is necessary when long-range and comparative studies are conducted. In this paper, we review how individual factors, such as (i) type of sampling strategy, (ii) representative sampling, (iii) seasonal variability, and (iv) which part of the plant is collected, may influence the concentration of trace elements in moss tissues and the level of uncertainty associated with sampling. In addition, we also discuss plant sample preparation techniques and how this may cause an uncontrolled element loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".