Interpreting prediction intervals and distributions for decoding biological generality in meta-analyses
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
Abstract Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 it