Patch–background contrast and patch density have limited effects on root proliferation and plant performance in <i>Abutilon theophrasti</i>
Bibliographic record
Abstract
Summary We examined biomass and root proliferation responses of Abutilon theophrasti Medic. to the density of high nutrient patches and the patch–background contrast. Contrast in nutrient content between a patch and the background soil, as well as patch density, are important features of heterogeneous soil environments that have received little research attention. Plants were grown in pots with no, one or two organic nutrient patches, and the equivalent nutrition of no, one or two patches in the background soil in a factorial design. Plant performance (root and shoot biomass) and root proliferation (root length inside and outside high‐nutrient patches) were measured. Root and shoot biomass increased with increasing nutrient heterogeneity, and root biomass declined with increased background soil nutrient availability. Patch–background contrast did not alter root or shoot biomass, nor allocation to roots. Biomass responses appeared to be driven by heterogeneity, as plants with access to the same total nutrients were larger when nutrients were concentrated in patches. The root proliferation response was not affected by either the density of patches or the degree of contrast. A conceptual model is presented describing how a plant's overall nutrient status could respond to changes in the patch–background contrast. The model predicts that nutrient‐sufficient plants should not respond to patches, but nutrient‐limited plants should proliferate roots proportionally to the contrast. The proliferation response should saturate when the total nutrients in both patch and background are no longer limiting.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".