Importance of measuring and reporting environmental conditions across plant science subdisciplines
Bibliographic record
Abstract
Understanding plant responses to the environment is based on research performed across several scales and subdisciplines. However, the interpretation and repeatability of experimental results depend on careful reporting of experimental procedures and environmental conditions. These conditions include light intensity and quality, temperature, relative humidity and vapor pressure deficit, soil water potential or volumetric water content, and pot size, which interact on plant physiological responses across biological and experimental scales regardless of whether they are the focus of the experiment. To ascertain how effectively and consistently these conditions are reported, we reviewed more than 200 plant science research articles on vascular plants published from 2020 through 2024. Environmental condition data were often not reported, including cases where the specific environmental variable was the focus of the study. This situation hampers both replicability and interpretability of results and hinders progress in understanding plant physiological responses across subdisciplines. The Environmental and Ecological Plant Physiology section of the American Society of Plant Biologists recommends several best practices to measure and report environmental conditions in plant physiology experiments, such as measuring and reporting actual environmental conditions, especially of control variables, to enable replication and comparative interpretation among experiments. These guidelines can aid authors in experimental design and manuscript preparation and assist reviewers in evaluating submitted manuscripts. Following such guidelines will enhance the dynamic progress of sound plant science within our community by improving replicability and enabling cross-disciplinary interpretation of results.
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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 | 0.542 | 0.692 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".