ssdtools v2: An R package to fit Species Sensitivity Distributions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it