Dry Matter and Nitrogen Partitioning Patterns in Bt and Non‐Bt Near‐Isoline Maize Hybrids
Bibliographic record
Abstract
While maize (Zea mays L.) hybrids with the Bt transgene from Bacillus thuringiensis have been gaining popularity, their dry matter (DM) production, N uptake, and whole‐plant N dynamics have not been assessed to justify their added cost. A field experiment conducted for 2 yr in Ottawa, Canada, studied DM and N partitioning patterns, and N‐use efficiency (NUE) of a conventional (Pioneer 3893) and its near‐isoline transgenic hybrid (Pioneer 38W36 Bt). The hybrids were grown with two N treatments (0 kg N [N0] or 150 kg N ha−1 with 15N‐labeled source [N150]). Plant samples were analyzed for DM, N concentration, and the fate of 15N at the V7, silking, and physiological maturity (PM) stages. Both hybrids were similar in harvest index, leaf chlorophyll content, and N concentrations and contents at the V7, silking, and PM stages. The Bt hybrid produced greater DM in leaves (42.1 vs. 37.5 g plant−1) and kernels (134 vs. 121 g plant−1) than its non‐Bt counterpart, it also accumulated about 11% more N in kernels and on a whole‐plant basis. Both hybrids had a similar partitioning of N and NUE in different plant parts. About 47% of the applied N was recovered at harvest, 70% of which was accumulated in the kernels of both hybrids. There was no indication that the Bt hybrid accumulated more N than its non‐Bt near‐isoline until the silking stage; the greater N content of the Bt hybrid at the PM stage was associated with greater DM in the kernels and leaves.
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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 | 0.001 | 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) | 0.001 | 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".