Overview and author contact information
Bibliographic record
Abstract
This chapter provides the reader with an overview of the scope of this textbook as well as the multifactorial issues that currently limit our understanding of the biomolecular mechanisms that are inherently associated with the chronic exposure of microbes, fish and humans to multiple pollutants over their lifetime.It is important to recognize that this complex problem is intricately associated with the nexus that exists between drinking water, food and resource extraction/utilization and has a local, regional and global dimension as 9 million people died in 2015 of environmental pollution-related causes.Tackling the pollutant exposure-adverse effects problem from a regulatory point of view is hampered on the one hand by temporal dynamic changes of pollutant concentrations in the environment (i.e. in drinking water, food and air) and by the complexity of biological organisms on the other.Thus making progress in addressing this multifactorial problem requires a clear focus on what organism and which pollutants one should focus on, to identify which exposure pathways are most relevant and how to unravel the biochemical mechanisms that unfold within the organism(s) involved.The reader will be made aware that important issues that need to be resolved pertain to environmental monitoring/sampling, the accurate quantification of pollutants (e.g.nanoparticles are exceedingly difficult to quantify in complex environmental matrices) and the related challenge of choosing an appropriate model biological system to study the effect of a certain class of pollutants. Overview of biochemical toxicology principles
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.667 | 0.606 |
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".