Self-declarations of environmental classification at Fass.se - Experiences from the reviewing process during 2017
Bibliographic record
Abstract
Since 2005 Sweden has a unique environmental classification system for pharmaceutical substances. It is a self-declaration system where each pharmaceutical company is responsible for their own environmental information, which is published on the open web-based portal www.fass.se. Prior to publication the environmental risk assessments are reviewed by IVL Swedish Environmental Research Institute (IVL) as an independent, external part. The present report describes the experiences from the review process during the year 2020. In 2020, environmental risk assessments (ERAs) were sent in for review 773 times. 68% of the reviewed assessments received the comment no remarks and were recommended to be published, whereas the other 32% were either recommended or needed to be corrected before publication. 694 unique substances were published at Fass.se during 2020. Of these substances 52% were exempted from classification, 27% were classified regarding environmental risk, and 21% could not gain any classification due to lack of data. The work of improving the review system is an on-going process. As a part of this work IVL performs studies to increase the knowledge of pharmaceuticals in the environment. During the last four years the focus for this type of work has been on proposing and developing a model for environmental risk assessment of pharmaceutical products. In 2020 the focus was on improving the understanding of different stakeholder needs by mapping roles and responsibilities as well as drivers, incentives and barriers for different actors along the value chain. The results were published in the report (B2395) “Reduce environmental impacts of pharmaceuticals along the value chain” in September 2020.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 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".