Natural and Synthetic Neurotoxins in Our Environment: From Alzheimer’s Disease (AD) to Autism Spectrum Disorder (ASD)
Bibliographic record
Abstract
Humankind has inadvertently designed a remarkably precarious combination of rapidly increasing and unchecked population growth with an increased liberation of novel and potentially pathogenic chemical compounds and neurotoxins into the biosphere. Parallel increases in the deleterious consequences of unrestricted population growth and diseasecausing toxic exposures in our environment are on the horizon. Globally this poses very significant socioeconomic and healthcare concerns that have been neglected for far too long. The perception of ‘human biochemical individuality’ indicates that certain individuals or human populations with specific genetic backgrounds may be predisposed to these toxic actions through either an acute or a more chronic type of life-long environmental exposure [11,33–36]. To cite one important example, since there is abundant evidence that neurotoxic compounds such as aluminum may play initiator or disease-propagating roles for AD, ASD and other progressive, age-related neurological diseases, then we should expect these kinds of exposure situations that can adversely affect human health and welfare to become even more common and widespread in the foreseeable future [7–27]. This may be particularly important socioeconomically and epidemiologically due in part to the excessive and additional burden it will place on our already strained healthcare system both here in the United States and in global situations where even basic healthcare systems remain underdeveloped or are simply unavailable to the local human population.
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 | 0.000 | 0.000 |
| 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".