Field decomposition of Bt-506 maize leaves and its effect on collembola in the black soil region of Northeast China
Bibliographic record
Abstract
Abstract The litters of Bt corn would go into the soil through straw returning and field ploughing after cultivation. To clarify whether the leaf litter decomposition rate and the non-target soil Collembola were influenced by the Bt protein or other litter properties in leaf litters of Bt corn in Northeast China, leaf litterbags of Bt-506, its near isoline Zheng 58 and a local type Zhengdan 958 were used in the field in Northeast China. The leaf decomposition rate, the leaf properties and the collembolan community in litterbags were investigated later. After seven months, only 43.5 ng/g Bt protein in Bt-506 leaf litter was left. All the investigated indices were not significantly different between Bt-506 and its near isoline Zheng 58. But when compared with local type Zhengdan 958, Bt-506 and its near isoline Zheng 58 contained lower non-structural carbohydrate content but higher total nitrogen content, and had lower litter decomposition rate and less abundance of Collembola. Collembolan abundance and litter decomposition rate were both significantly correlated with the non-structural carbohydrate and total nitrogen contents of the leaf litters. Field study results revealed Bt protein did not affect the leaf litter decomposition rate and the collembolan community in leaf litterbags in short term. The significant differences of these investigated indices among corn types were caused by the different non-structural carbohydrate and total nitrogen contents in leaf litter.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".