Bibliographic record
Abstract
efore Fernando Miguez began running experiments as a University of Illinois master's student, like any good scientist he dove first into the research literature.His subject was the effect of winter cover crops on summer corn yields, and by the time Miguez entered grad school, a healthy body of work already existed.So, he sat down to review a stack of studies, thinking, naturally enough, that he'd soon hit upon a knowledge gap to target in his trials.He thought wrong."To be honest, it seemed like the more papers I read, the more confused I was," says the ASA and CSSA member, now an assistant professor at Iowa State University.Yields varied widely by year and with local climate and soil conditions, leaving him unable to discern any clear trends.Eventually, he gave up and chose a different tack."I thought, 'Let's try to do a meta-analysis on this topic,' " he says, "because reading more papers is not helping."Meta-analysis-a statistical technique for combining and analyzing the results from 10 or 20 to hundreds of studies-has been practiced for decades, and in some fields, such as medicine, its use is routine.The principle behind it is that scientific debates, even small ones, are never resolved by a few experiments.Instead, "it's the collection of results from many sources that move science forward and inform our decision-making," says Ohio State University plant pathologist and meta-analysis expert, Larry Madden."Science is meant to be a cumulative process."Done right, meta-analysis is simply the most robust, objective means to conduct this process, Madden adds, particularly when studies say different things, as in Miguez's case."It's a way to look at an entire collection of published papers and try to make general sense of them," agrees Chris van Kessel, a University of California-Davis agronomist, experienced meta-
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.
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 | 0.207 | 0.458 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".