Bibliographic record
Abstract
Recent work suggests that resource economic traits might help predict the strength and direction of plant-soil feedback interactions, both in natural systems and in agriculture. However, there are many competing hypotheses to explain the effects of plant resource economics on plant-soil feedbacks. Faster-growing plants may have positive fertilizing effects if their tissues are incorporated and mineralized by soil microbes, but may also have negative effects if pathogens build up, or if fungal symbionts are lost through fertilization. Identifying the direction of effects may be confounded if nutrients are exported through herbivory, leaching, or crop harvesting. To determine causality in the effect of plant traits on plant-soil feedbacks it is essential for plant-soil feedback experiments to (1) quantify the mass of nutrients held in standing, or harvested plant biomass, and in losses to other sources in the field, and (2) undertake soil chemistry measurements (e.g. gross and net nitrogen mineralization) of nutrients limiting for plant growth throughout all phases of the feedback cycle. If rigorous nutrient budgeting in plant-soil feedback research is more widely practiced this will provide the data needed to synthesise results in comparable ways, and will enable mechanistic insights into the role of plant traits in mediating plant competition in both natural and applied settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".