Decomposition of grain-corn residues (<i>Zea mays</i> L.): A litterbag study under three tillage systems
Bibliographic record
Abstract
This study was undertaken to obtain litterbag decomposition data for grain-corn residues in eastern Canadian conditions, to determine tillage and/or depth effects on residue mass loss, and to compare decomposition patterns for the different plant parts that constitute the residue (cobs, stems, leaves, husks). Mesh bags containing residues were buried or left on the soil surface in grain-corn plots under no-till, reduced tillage, and conventional tillage, and retrieved over a 2-yr period. Data were obtained separately for each plant part, then used to calculate pooled totals for all residues combined, for all residues except cobs, or for stems and leaves only, to facilitate comparison with studies based on different residue mixes. Buried residues lost mass faster than surface residues. Despite low overwinter temperatures, residue mass decreased substantially between placement in November and first sampling in mid- May. Surface litterbag residues lost 20% of initial mass during this period, residues buried at 5 cm lost 33%, and those at 20 cm lost 41%. Corresponding losses from mid-May to mid-October were 21, 42 and 32%, respectively. Mass loss was fastest for buried leaves, husks and stems (89-98% loss in 2 yr) and slowest for surface cobs (32% loss in 2 yr). Key words: Corn, maize, crop residue decomposition, litterbag, no-till, tillage
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".