Bibliographic record
Abstract
Known clusters for each of the diseases under discussion exist: two for Parkinson's disease, one (maybe two) for ALS, and one for Alzheimer's disease. The presence of widespread NFTs in both lytico and bodig was one of the more surprising and intriguing facets of amyotrophic lateral sclerosis-parkinsonism-dementia's (ALS-PDC) pattern of expression. One involved skin in the form of collagen disorganization, and the other involved diaphyseal aclasis or cartilage-capped benign bone tumors (exostoses). The putative link between cycad consumption and ALS-PDC led to a detailed search over a number of years for potential neurotoxins in cycad seeds. The notion that BMAA, or any other molecule for that matter, is causal to ALS-PDC is one that clearly has its adherents. The list of potential toxins involved in ALS-PDC include some toxins associated with fishing using powdered Barringtonia asiatica, known as "the fish-kill plant", zinc from galvanized pails used for washing cycad seeds, and a host of others.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".