Cycad Toxins and Neurological Diseases in Guam: Defining Theoretical and Experimental Standards for Correlating Human Disease with Environmental Toxins
Bibliographic record
Abstract
Consumption of Cycas micronesica seed tissue has been associated with the amyotrophic lateral sclerosis–parkinsonism dementia complex (ALS–PDC) of the Western Pacific. However, failures to document vital plant and neighborhood descriptors and pronounced variability in toxin concentrations noted within and among studies obfuscate decades of research on this subject. We discuss the theoretical and experimental constraints of plant tissue sampling in relation to human disease research. Comparisons are made between this approach and methods used throughout the history of ALS–PDC research, most notably very recent reports concerning β-methyl-amino-alanine. Methods for studying possible plant neurotoxins need to be standardized and must follow rigorous criteria to be valid in principle. Our discussions reveal why these criteria are essential and highlight the impact that natural variations have on environmental toxin quantification and interpretation. Past research on cycad toxins is deficient on experimental and theoretical grounds, and interpretation of published data is dominated by ambiguities. This area of study as conventionally conceived and carried out needs transforming. We argue that future empirical studies should honor appropriate plant science standards concomitantly with medical science standards. This dual focus will ensure appropriate sampling scheme, sample size, and reporting of background plant and community factors known to influence phenotypic plasticity.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".