Soybean: Evaluation of Inoculation
Bibliographic record
Abstract
Most of the nitrogen (N) required by a soybean plant is supplied via biological nitrogen fixation (BNF). When BNF is adequately established in the soil, soybean can obtain up to 50 to 75% of its N from the air. This project aims to quantify the response to inoculation for soybean in its second year in a field without previous history of this crop. Due to this objective, a field study was conducted during the 2015 and 2016 growing seasons at Ottawa, KS (East Central experiment field location). The treatments consisted of five different N-management approaches: non-inoculated (NI), inoculated ×1 (I×1), inoculated ×2 (I×2), inoculated ×3 (I×3), and non-inoculated but fertilized with 300 lb N/a (NF) as the main N source. In 2015, yields among treatments did not differ significantly from one another. In 2016, yields ranged from 36 to 59 bushels per acre. Greater yields were recorded when fertilized with 300 lb N/a, while lowest yield was related to the non-inoculated scenario. Treatments presented significant yield difference; however, the scenario with 300 lb N/a did not differ from the inoculated ×3; while the inoculated treatments were not different for the yield factor. In summary, further research should be pursued to be more conclusive as to the best management approach for N in soybeans in an area without history of this crop.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".