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.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".