The influence of biofertilizers on leaf economics spectrum traits in a herbaceous crop
Bibliographic record
Abstract
Microbial inoculations or 'biofertilizers' represent novel contributions to sustainable agriculture. While belowground mechanisms surrounding how biofertilizers enhance crop production are well described, their role in aboveground trait expression remains less well explored. We quantified infraspecific variation in leaf economics spectrum (LES) traits in response to 10 biofertilizer treatments in basil (Ocimum basiclicum) cultivated under hydroponic conditions. Multiple physiological (i.e. maximum photosynthesis rates (A), dark respiration (R), and leaf-level light compensation points) and morphological (i.e. leaf mass per area (LMA) and leaf thickness) traits varied significantly across microbial treatments. Following treatments, basil plants differentiated from one another along an infraspecific LES, with certain plants expressing more resource-acquiring LES trait values (i.e. high A, R, leaf N, and low LMA), versus others that expressed the opposite suite of resource-conserving LES trait values. Infraspecific trait covariation largely matched LES patterns observed among plants globally. Bivariate and multivariate trait analyses further revealed that certain treatments-namely those including closely related Bacillus and Brevibacillus species strains-increased leaf resource capture traits such as A and leaf N. Biofertilizers influence plant performance through a role in moderating infraspecific leaf trait variation, thereby suggesting aboveground leaf traits may be used to diagnose optimal biofertilizer formulations in basil and other crops.
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 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 | 0.000 | 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) | 0.000 | 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".