Assessment of PBTs in the European Union: a critical assessment of the proposed evaluation scheme with reference to plant protection products
Bibliographic record
Abstract
A number of international and national programs classify substances that are persistent (P or very P), bioaccumulative (B or very B), toxic (T), or have the potential for long-range transport. The oldest of these programs is the Stockholm Convention on Persistent Organic Pollutants. More recent programs address persistent, bioaccumulative, and toxic (PBT) properties for chemicals in general (Registration, Evaluation, Authorisation and Restriction of Chemicals, REACH; EC 253/2011) and plant protection products (PPPs) (EC 1107/2009). However, these programs used different criteria for categorization. We critically assessed the criteria and process used in the categorization of PPPs and noted that EC1107/2009, in contrast to the Stockholm Convention or REACH, offers no process for carrying out a further, more refined assessment of those pesticides that are identified as having PBT properties. Thus, in contrast to REACH, few basic screening criteria are used for final-step management decisions. Guidance on the selection of data is not provided, and the criteria used are unclear. For example, no guidance is given as to how the half-lives in soil, water, and sediment should be derived and the term ‘half-life’ is not clearly defined. Large amounts of useful data on environmental and toxicological properties are available for PPPs but most of this is not used in the categorization, for example, photolysis in water, water-sediment, and on soil, important environmental degradation processes particularly relevant to pesticides. The criteria for bioaccumulation and toxicity appear to be focused only on aquatic ecosystems and do not address the terrestrial compartment which is particularly relevant for pesticides and potentially relevant for PBT considerations. The categorization process under EC 1107/2009 could be made more efficient and reduce false negatives and positives if a formal weight of evidence approach was applied to multiple lines of evidence. This paper presents these ideas and how they can be incorporated into the framework for categorization to better classify plant protection products in terms of persistence, bioaccumulation, and toxicity.
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 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 | 0.151 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".