Created by Peter Downing – Educational Media Access and Production © 2011 • One
Bibliographic record
Abstract
important source of EDCs in the environment are municipal wastewater effluents (MWWEs)4,5 as conventional wastewater treatment technologies are often incomplete or inefficient at removing these contaminants. • There is concern for organisms, such as fish, that live in receiving water bodies in the southern prairies as these water bodies are often small and can consist of up to 100 % effluent during dry seasons (e.g. Wascana Creek, Regina, Saskatchewan). • Over the past decades, there is an increasing concern regarding the release of emerging contaminants such as pharmaceuticals and personal care products (PCPPs), endocrine disrupting compounds (EDC’s), pesticides and brominated flame retardants (BFRs) into the environment.1 • Among these chemicals, EDCs have received particular attention as they have been shown to interact with the endocrine system resulting in adverse effects on endocrine homeostasis, reproduction, development and/or behaviour. 2,3
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.777 | 0.599 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".