Soybean Evaluation of Inoculation: A Three-Year Summary
Bibliographic record
Abstract
The relationships between soybean (Glycine max) seed yield and response to nitrogen (N) fertilization have received considerable coverage in scientific literature. This project aims to quantify the response to inoculation for soybean in a field without previous history of this crop (20 years). To address this objective, field studies were conducted during the 2015, 2016, and 2017 growing seasons at the East Central Experiment Field, Ottawa, KS. The treatments consisted of five different N-management approaches: non-inoculated (NI), inoculation at the recommended commercial rate (I1), a double rate of inoculation (I2), a triple rate of inoculation (I3), and non-inoculated but fertilized with 300 lb of N/a (NF). In the 2015 growing season, yields did not statistically differ from one another. In the 2016 growing season, treatment differences were observed and seed yield ranged from 36 to 59 bu/a. In the 2017 growing season, treatments showed significant yield difference, with yields ranging from 23 to 52 bu/a, from the NI to the NF treatment, respectively. Further research should be carried out to understand the impact of the inoculation practice and better understand the best management for N in soybean in newly-planted areas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".