Evaluation and Further Development of EASE Model 2.0
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
EASE (Estimation and Assessment of Substance Exposure) is a general model that may be used to predict workplace exposure to a wide range of substances hazardous to health. First developed in the early 1990s, it is now in its second Windows version. This paper provides a critical assessment of the utility and performance of the EASE model, and on the basis of this review, recommendations for the structure of a revised model are outlined. Twenty-seven stakeholders were interviewed about their previous use of EASE, perceived advantages and limitations of the model and suggestions for improvement. A subset of stakeholders was contacted on a second occasion to determine their views on the preferred outputs for an ideal exposure assessment model. Overall, stakeholders felt that the model should be updated to provide more accurate and precise exposure assessments. However, users also expressed the view that the simplicity and usability of the software model should not be compromised. Six studies investigating the validity of the inhalation exposure assessment section of EASE were identified. These showed that the model generally either predicted close to the measured exposures or overestimated exposure; though performance was highly variable. Two studies investigated the validity of the dermal exposure assessment and found that EASE produced considerable overestimates of actual dermal exposure (the amount of a substance that actually lands on the skin). A conceptual model of exposure was developed to investigate whether the structure of the EASE model is appropriate. Although EASE has a number of characteristics that describe exposure, it is a greatly simplified model and does not include all the important exposure determinants. More importantly, EASE can produce estimates of exposure that are ambiguous or incomplete. Our conceptual model may provide a rational basis for developing an improved version of EASE but further consultation is needed to decide the purpose and intended use of any successor to EASE.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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