Comparison of models for predicting pore space indices and their relationships with CO<sub>2</sub> and N<sub>2</sub>O fluxes in a corn–soybean field
Bibliographic record
Abstract
Several models predict soil pore space indices (the relative gas diffusion coefficient, Ds/Do and the pore tortuosity, τ), but information is lacking on which models predicted indices better relate to soil processes. We compared pore space indices’ predictive models based on air-filled porosity (fa) alone vs. models using air-filled porosity and total pore space (Φ) (fa + Φ). We also assessed the relationships between these indices and CO2 and N2O. The study was conducted from 2011 to 2014 on a silt loam soil at Lincoln University. Soil samples were collected at 0–10 and 10–20 cm depth and oven dried at 105 °C for 72 h. After drying, fa and Φ were calculated and later used in models for predicting Ds/Do and τ. CO2 and N2O were measured with a Shimadzu gas chromatograph (GC) and a photoacoustic gas analyzer (PSA). Results showed that Ds/Do predicted using fa alone (Marshall and Buckingham) was higher as compared with values predicted with models based on fa + Φ (Sallam et al., Millington, and Jin and Jury) (P < 0.001). However, values of τ predicted with models based on fa alone were lowest (P < 0.001). CO2 and N2O measured with GC better related with Ds/Do and τ.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".