Block‐Recursive Path Models for Rooting‐Medium and Plant‐Growth Variables Measured in Greenhouse Experiments
Bibliographic record
Abstract
Core Ideas Path analysis reveals multidirectional causal relationships in systems of variables. Variable selection and model modification result in plausible path diagrams. Softwood biochar water‐extractable components enhance corn P uptake and root length. Biochar water and nutrient holding capacities positively affect corn N and Ca uptake. WEBC and WNHC directly affect corn dry weight and K content, in opposite ways. Searching for statistically significant and biologically relevant relationships in complex datasets is generally a difficult task, in many fields including agronomy. Path analysis is an accessible, graphical, and inferential method for multivariate data exploration, which allows for more than unidirectional causal relationships between the measured variables. Here, data from experimental rooting media, and from corn plants grown in a greenhouse experiment, are used to introduce the theory and methodology of path analysis. Block‐recursive path diagrams are hypothesized for each set of variables measured from the rooting media and the plants. A path model is hypothesized for the union of the two sets of variables. Several reciprocal relationships between rooting medium and plant variables are pointed out, which challenge the usual assumption of unidirectional causality. Besides other relationships between experimental variables, modeling the union set of variables led to the conclusion that the measurement of any of the variables from this experimental system gives information about all of the other experimental variables. The diagrams that are presented include covariances between disturbance terms, which suggest the presence of shared unmeasured causes of variation. In some other cases, such covariance indicates shared variation due to the measurement of variables with the same machine.
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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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".