Water vapor transport through bioenergy grass residues and its effects on soil water evaporation
Bibliographic record
Abstract
Abstract Miscanthus is a productive perennial grass that is suitable as a bioenergy crop in “marginal” lands (e.g., eroded soils) with low water holding capacity. However, little is known about the impact of miscanthus residues on vapor transport and soil water budgets. Laboratory experiments were conducted to measure the vapor conductance through miscanthus residues and its effect on soil water evaporation. The ranges for the length, width, and thickness of residue elements were 0.5–9.0, 0.1–0.5, and 0.1–0.5 cm, respectively. Average residue areal, bulk, and skeletal densities were 0.88 kg m −2 , 24 kg m −3 , and 1006 kg m −3 , respectively, giving a porosity of 0.98 m 3 m −3 . A power function described the decrease in conductance with increasing residue load. The corresponding conductance for a residue load of 0.88 kg m −2 was 1.6 mm s −1 . During the first days of a 60‐day drying experiment, cumulative evaporation showed logarithmic decay with increasing residue load. Conversely, cumulative evaporation during the last days of the study showed little difference between treatments. Measurements indicated that there is a “critical” residue load (∼1.0 kg m −2 ) beyond which evaporation no longer decreases appreciably when the soil is under the stage 1 evaporation regime. Results suggest that soil water conservation in marginal lands may be accomplished by maintaining moderate amounts of bioenergy grass residue covering the soil. Determining “critical” loads for different residue types is a knowledge gap that merits further research.
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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 | 0.000 | 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 | 0.000 |
| 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".