Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study of causal relationships is important when addressing questions of efficacy of treatment interventions and etiology of disease. The evaluation of a cause-and-effect relationship between exposure to a putative causal factor and outcome can be undertaken using a variety of study designs including randomized controlled trial and cohort and case control studies. Study participants should be selected in a manner that minimizes bias and confounding and is representative of the target population. Confounding can be controlled by using several strategies including restriction, randomization, stratification, matching, and multivariable analyses. The degree of association is then summarized by the relative risk for prospective studies and the odds ratio for retrospective studies. The precision of these estimates should be indicated by providing their confidence intervals. Important indicators of causation are correct temporal and dose-response relationships between exposure and outcome, a large magnitude in the strength of association, and consistency and specificity of association. Biological and epidemiological sensibility and analogy to other well-established relationships provide additional support for a causal hypothesis.
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.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it