A systematic review of the statistical scope of restoration ecology of invaded grassland ecosystems
Bibliographic record
Abstract
Restoration ecology is a rapidly growing field of research. The statistical analyses and experimental designs used in this field have likely also expanded. In this review, the statistical scope of the restoration ecology of invaded grasslands will be investigated. A systematic review was conducted on 103 articles to examine the types of statistical tests used and how they changed over time, if assumptions are tested, and how the number of statistical tests and the experimental design influence both the citation rate of articles and the impact factor of journals where these articles are published. ANOVAs have consistently been the dominant test. Statistical test diversity has increased since the year 2000. Most articles did test the assumptions of statistical analyses. The number of tests, and sample size of experiments are both positively correlated with the average citation rate of articles and the impact factor of the journal while the number of factors was negatively correlated. GLMs are recommended as a statistical test to be used more frequently in the future over ANOVAs. There is room for improvement in terms of reporting statistics accurately, including testing assumptions. When possible, sample sizes should be increased to both increase the quality of data, and the citation rate and the journal impact where articles are published. When possible and appropriate, sample sizes and the number of statistical tests should be increased. Adding factors in experimental designs should only be done so without compromising sample size as it has been shown to hinder the citation rate and journal impact.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".