Bibliographic record
Abstract
We dedicate this special issue on the challenges associated with assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment, to the memory of Dr Theodora (Theo) Colborn. Theo was a pioneer in the science of the effects of low-dose exposures to environmental chemicals and, for the past 25 years, was instrumental in the development and integration of the field of endocrine disruption. Theo introduced us to one another about 4 years ago which led to the founding of Getting to Know Cancer, and ultimately the launch of the Halifax Project (which has been a tremendously productive collaboration for the integration of cancer biology and environmental toxicology). So we want to thank her here for her legacy of work in this area and her influence and encouragement on our own research. Theo was well known internationally for her tireless commitment to the protection of public health, but not everyone knew that she was also a tremendously generous and insightful scientist who assembled researchers from a variety of specialties in developmental biology and allowed them to discover for themselves what she had understood about the influences of low-dose exposures to certain environmental chemicals on embryonic and fetal development. Indeed, she nurtured cross-disciplinary collaboration and it was that collegiality and spirit of sharing that produced seminal insights that opened up the entire field of endocrine disruption. So we have attempted to use a similar approach to help us understand the importance of ongoing low-dose exposures to mixtures of chemicals in the environment and their relevance for cancer and carcinogenesis. In other words, this is truly an extension of her work, and we want to pay tribute and offer thanks for her wisdom, her generosity and her legacy.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.127 | 0.091 |
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".