ssdtools v2: An R package to fit Species Sensitivity Distributions
Bibliographic record
Abstract
Species sensitivity distributions (SSDs) are cumulative probability distributions that are used to estimate Hazard Concentrations (HC ) -the concentration of a chemical that is expected to affect a given % of species.HC 5 values, which are intended to protect 95% of species, are often used for the derivation of environmental quality criteria and ecological risk assessment for contaminated ecosystems (Posthuma et al., 2001).The Hazard Proportion (HP ) is the proportion of species affected by a given concentration .ssdtools is an R package (R Core Team, 2024) to fit SSDs using Maximum Likelihood (Millar, 2011) and estimate HC and HP values by model averaging (Schwarz & Tillmanns, 2019) across multiple distributions (Thorley & Schwarz, 2018).The shinyssdtools R package (Dalgarno, 2021) provides a Graphical User Interface to ssdtools.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.114 | 0.062 |
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".