Screening of potential phosphate solubilizing bacteria inoculants should consider the contrast in phosphorus bio-solubilization rate along with plant growth promotion and phosphorus use efficiency
Bibliographic record
Abstract
AIMS: Although phosphate solubilizing bacteria (PSB) have been globally reported to improve soil phosphorus (P) availability and plant growth, technical gaps such as the lack of an ideal screening approach, is yet to be addressed. The potential of non-halo-forming PSB remains underestimated because of the currently adopted screening protocols that exclusively consider halo-forming and PSB with high phosphorus solubilization (PS) capacities. Yet, caution should be taken to properly assess PSB with contrasting PS rates regardless of the presence or absence of the solubilization halo. METHODS AND RESULTS: This study sought to examine the PS rate and plant growth promotion ability of 12 PSB categorized as high PSB (H-PSB), medium PSB (M-PSB), and low PSB (L-PSB) based on their PS rates of rock phosphate (RP). The non-halo-forming PSB Arthrobacter pascens was categorized as H-PSB, which might have been eliminated during the classical screening process. In addition, induction of organic acids and phosphatase activity in rhizosphere soils by H-, M-, and L-PSB was proportional to increased wheat P content by 143.22, 154.21, and 77.76 mg P g-1 compared to uninoculated plants (18.1 mg P g-1). CONCLUSIONS: Isolates considered as M- and L-PSB could positively influence wheat above-ground physiology and root traits as high as H-PSB. In addition, non-halo-forming PSB revealed significant PS rates along with positive effects on plant growth as high as halo-forming PSB.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".