Microbial activity and hard red spring wheat growth improvement following biostimulant application
Bibliographic record
Abstract
Abstract Reclamation of oil and gas disturbed soils is challenging due to diminished function (i.e., soil physical, chemical, and biological properties) from the loss of soil organic carbon (SOC) and potential mixing of topsoil and subsoil. Biostimulants are agro‐products applied to soil to improve SOC formation, microbial nutrient cycling, and crop yields, suggesting their potential use in reclaiming oil and gas disturbed soils. However, studies on the ability of biostimulants to enhance reclamation in disturbed soils are limited. Therefore, research was conducted to determine if biological properties were affected by biostimulant products in soil collected from an active pipeline installation project. The study was conducted in a greenhouse using pots consisting of the following soil treatments: TS100 (100% topsoil), TS50 (1:1 by‐weight subsoil/topsoil), TS25 (3:1 subsoil/topsoil), TS12.5 (7:1 subsoil/topsoil), and TS0 (100% subsoil). Blended soil either received a liquid inoculant or biotic mulch biostimulant and were planted with hard red spring wheat (Triticum aestivum) later on. Soil biological properties were generally influenced by topsoil concentration where TS50 consistently produced similar results to TS100, however, nitrogen (N) and phosphorus (P) were also influenced by biostimulant treatment. Additionally, wheat biomass was significantly greater in the liquid treatment, whereas the biotic mulch stimulated greater microbial abundance and activity. Overall, increased topsoil improved biological recovery in the short term, and the addition of biostimulants in blended soils can also enhance recovery regardless of topsoil content. However, it is unclear whether the recovery is sustained into the long‐term without additional biostimulant application.
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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".