An experimental approach for quantifying the spatial interactions of plants under different treatment conditions
Bibliographic record
Abstract
:Spatial statistics were used in conjunction with standard plot experimentation to form an approach geared towards testing hypotheses about plant spatial interactions under different treatment conditions. Specifically, correlograms were used to quantify the changing patterns in the spatial distribution of plant size (total vegetative length) among pre-planted individuals within a sand box. A repeated-measures ANOVA procedure was then utilized to test for significant differences between the treatment effects as they evolved over time, where the experimental unit was the correlogram value obtained from within each sand box at a specific lag distance. Separate ANOVA tests were also executed for each spatial lag distance and temporal sampling period. In an example demonstrating both procedures, it was shown that a water-stress treatment provoked positive spatial autocorrelation to emerge up to 24 cm from Panicum amarum plants, while a normal water treatment resulted in no change in spatial structure. The difference between the two treatments, and the spatial correlograms of their total vegetative length values, were significant (P = 0.0007). The temporal divergence of the treatments, as spatial patterns evolved, was also significant (P < 0.0001). The approach discussed in this experiment is well suited for evaluating the statistical significance of treatment effects upon the spatial structure of interacting individuals, in both greenhouse and natural settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".