The measurement and quantification of generalized gradients of soil fertility relevant to plant community ecology
Bibliographic record
Abstract
Abstract We propose an operational definition of soil “fertility” that is applicable to plant community ecology and develop a method of measuring and quantifying it, using structural equations modeling, that is generalizable to soils in different regions whose fertility has different causes. To do this, we used structural equation modeling (SEM). The measurement submodel predicts the latent “generalized fertility,” F G , of a soil using four indicator variables: the relative growth rates of Festuca rubra , Trifolium pratense , Triticum aestivum , and Arabidopsis thaliana . The direct causes of F G in this study were the supply rates of NO 3 − , P, and K as well as three indirect causes consisting of three physical soil properties, but these can change between studies. The model was calibrated using 76 grassland soils from southern Quebec, Canada and independently tested using aboveground net primary productivity (NPP) of the natural vegetation over two growing seasons. Both the measurement submodel and the full SEM fit the data well. The F G values predicted 51% of the variance in NPP and were a better predictor than any other single variable, including the actual nutrient flux rates. Furthermore, this model can be applied to grassland soils anywhere because of its modular nature in which the causes and effects of soil fertility are clearly separated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".