Advancing plant ecology through meta‐analyses
Bibliographic record
Abstract
Summary The inherent complexity of nature produces a diverse and varied set of outcomes for any given ecological process. However, the advance of ecology requires making generalizations that synthesize current knowledge and guide new basic research and practical applications. Among the synthesis tools available for this specific purpose, meta‐analysis is one of the most accurate and powerful methods. This Special Feature examines the use that meta‐analysis has received in plant ecology over the last two decades and provides examples of synthesis applied to contemporary topics in different areas of plant ecology from populations to ecosystems. The number of meta‐analyses in plant ecology has been increasing rapidly in the last two decades. However, this increase has not been accompanied by a parallel increase in quality. The opening review paper in this Special Feature provides a checklist of quality criteria specific to ecological meta‐analysis that will largely contribute to improvement of the methodological and reporting standards of meta‐analyses. The following five papers in the Special Feature demonstrate the advantages of application of meta‐analysis compared with other techniques of research synthesis. Meta‐analysis is applied here to demonstrate the consistency of ecological hypotheses across large spatial scales (e.g. Janzen‐Connell hypothesis), understand sources of variation in the magnitude of ecological processes (e.g. herbivory effects on leaf life span, effects of intraspecific genetic diversity on communities and ecosystems), measure synergistic impacts of environmental change drivers (e.g. CO 2 , drought, land use) or assess research gaps within a certain sub‐discipline of plant ecology (e.g. landscape fragmentation). Synthesis . Meta‐analysis can contribute to the advance of ecological theory by synthesizing the available evidence on specific topics and informing the scope of generalizations. However, plant ecologists can only take full advantage of this capacity if we improve our knowledge on how and when to conduct a proper meta‐analysis, and by avoiding the frequent misuses that have characterized the use of this statistical tool in the ecological literature thus far.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".