A measure of generalized soil fertility that is largely independent of species identity
Bibliographic record
Abstract
BACKGROUND AND AIMS: In 2019, Daou and Shipley produced an operational definition of 'generalized' soil fertility (FG) for plant community ecology and quantified FG using a structural equation model (SEM) invoking a single latent variable. We evaluate a critical assumption of this model: that FG is generalizable to any combination of plant species; i.e. that any combination of plant species will respond in the same direction to the soil 'fertility' gradient in terms of growth. METHODS: We grew nine widely different species singly in each of 25 soils from southern Quebec, Canada, whose FG value had been previously quantified. The original SEM was tested using every possible combination involving from four to nine species. KEY RESULTS: The assumption was rejected due to a subset of three species that responded to a second latent dimension. We then proposed an alternative model that includes FG plus a second latent variable that measures species' deviations from FG due to specific adaptations to soil pH. This alternative model was consistent with every combination of up to eight species. The predictions of FG when ignoring this second dimension and when using the new model were extremely correlated (r =0.98). CONCLUSIONS: The initial unidimensional model of Daou and Shipley was successful in non-acid soils but not in soils with extreme pH and when species specifically adapted to such extreme soils were included. The alternative two-dimensional model takes into account these exceptions and is consistent with the notion of shared physiological niche responses along a gradient of generalized soil fertility.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".