A cautionary note on the use of species presence and absence data in deriving sediment criteria
Bibliographic record
Abstract
In recent years, a variety of approaches to deriving sediment quality guidelines have been developed. One approach relies on establishing an empirical relationship between the concentration of a contaminant in sediment and the condition of some biological indicator, for example, combining measured sediment concentrations of contaminants combined with data on colocated benthic species to measure in situ community effects of contamination. Biological threshold concentrations derived in this manner are being considered or have already been adopted by some regulatory agencies as a means for deriving sediment guidelines (e.g., Canada's Provincial Sediment Quality Guidelines). In order to test the validity of this method, we constructed several Monte Carlo simulations to illustrate that the methodology used to develop these guidelines is flawed by the effects of sampling and statistical artifacts that emerge from undersampling a lognormal density function. As a case study, this paper will present the screening level concentration method used by the Ontario Ministry of the Environment (Toronto, ON, Canada) and provide the results of several probabilistic exercises highlighting these issues. We present a word of caution on the applicability of methods that rely exclusively on statistical and mathematical relationships between invertebrate data and sediment concentrations to derive sediment quality guidelines.
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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 | 0.150 | 0.434 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.006 | 0.025 |
| 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".