There are no whole truths in meta‐analyses: all their truths are half‐truths
Bibliographic record
Abstract
In a recent letter, Thomsen & Wernberg (2015) rean-alyzed data compiled for our recent paper (Lyonset al., 2014). In that paper, we examined the effectsof macroalgal blooms and macroalgal mats on sevenimportant measures of community structure and eco-system functioning and explored several ecologicaland methodological factors that might explain someof the variation in the observed effects. Thomsen &Wernberg (2015) re-analyzed two small subsets of the data, focusing on experimental studies examining effects of blooms/mats on invertebrate abundance.Their analyses revealed two interesting patterns.First, they showed that macroalgal blooms reducedthe abundance of communities that Thomsen andWernberg categorized as ‘mainly infauna’, whileincreasing the abundance of communities categorized as ‘mainly epifauna’. Second, they showed that theimpacts of macroalgal blooms on ‘mainly infauna’communities increased with algal density in experiments that included multiple levels of algal density.These findings, as well as the conclusions that Thomsen & Wernberg (2015) draw from them, are largely consistent with our own expectations and interpretations. However, we also feel that some caution is required when interpreting the results of their analyses.
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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 | 0.171 | 0.496 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".