Spatial and Temporal Variability of Extractable Lipids as Influenced by Cropping History
Bibliographic record
Abstract
Tillage and crop rotations may alter soil organic matter (SOM) content and quality. Organic matter content can be satisfactorily assessed by determining total organic matter and C content, but easily obtainable, reliable, and highly sensitive indicators of its quality are still lacking to assess its biological, chemical, and physical functions. The spatial and temporal distributions of diethyl ether–(DEE) and chloroform‐(CHCl 3 ) extractable lipids and gravimetric DEE/CHCl 3 and CHCl 3 /total extractable lipids (TEL) ratios were evaluated as indicators of organic matter quality in two soils of Agri‐Food Canada cropping system fields situated in Qu e ´ bec. Diethyl ether and TEL were significantly ( P ≤ 0.001) more abundant under perennial than annual crops, indicating that the perennial crops were better suppliers of easily biodegradable organic matter. Chloroform‐extractable lipids remained relatively constant under both crop rotations, indicating that soil biochemical inertness was relatively the same. Spatial and temporal distributions of these indicators of soil organic matter quality closely correlated with total organic C and clay contents. The DEE/CHCl 3 and CHCl 3 /TEL ratios proposed for assessing organic matter quality were very sensitive ( P ≤ 0.01) in detecting changes in SOM status resulting from crop rotations and tillage practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".