Bibliographic record
Abstract
e've all heard stories about tea leaves having the power to forecast the future.But what if tea could actually help you remember the past?Alzheimer's disease (AD) is an incurable form of progressive dementia that claims people's abilities to remember family, friends -and the past.Currently, in Canada, more than a quarter million Canadians (280,000 people) are losing their memories to AD.By 2031, more than 750,000 Canadians are expected to have the disease and related dementias.And it doesn't stop there.AD also has a profound impact on Canada's economy, with caring costs alone accounting for $5.5 billion each year (Ostbye and Crosse 1994).For the past few years, a number of researchers have conducted studies on tea leaves to prove that they can protect the brain's neurons against AD's toxic compounds.Some have found encouraging results -especially with green tea.With the help of Dr. Stéphane Bastianetto at Montreal's Douglas Hospital Research Centre, I undertook a study that proves for the first time that regular consumption of both green and black teas can prevent neuron cell death or delay the onset of AD.If you have any questions regarding research discussed in this article, please feel free to contact Dr.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".