Soybean: Evaluation of Inoculation
Bibliographic record
Abstract
A soybean crop can obtain up to 50 to 75% of its nitrogen (N) requirements from the air when the biological fixation is effectively established (Pedersen, 2007). The overall objective of this project was to quantify the response to inoculation for soybean in a field without previous history of this crop. Following this rationale, a field study was conducted during the 2015 growing season at Ottawa (east central Kansas). The treatments consisted in five different N management approaches: non-inoculated, inoculated × 1, inoculated × 2, inoculated × 3 and non-inoculated but fertilized with 300 lb N per acre as the main N source. The study was planted in an area without soybean history, the grain yield ranged from 26 to 29 bushels per acre. Greater yields were recorded when a double inoculation rate was applied (× 2), while lowest yield was related to the non-inoculated scenario. However, statistically, treatments did not present any significant yield difference. In summary, further research will be performed to be more conclusive as related to the best management approach for N in soybeans when first planted in fields without previous history of this crop.
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 | 0.000 |
| 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".