Impacto del uso del suelo sobre las fracciones físicasde materiaorgánica en tres Ferrasols de pendientesen México
Bibliographic record
Abstract
The effects of cultivation in hillside (> 30% slope) on soil C and N stocks after forest clearance and subsequent cultivation in new re-growth forest sites not always induce rapid soil organic matter (SOM) loss in subtropical areas. In the present study we evaluated the sensitivity to SOM changes of material floatable in water (Fw), coarse sand (> 250 µm), fine sand (50-250 µm), silt (2-50 µm) and clay (0-2 µm) fractions by the impact of cultivation of Mexican Ferrasols. This research also determined the relative degree of C and N saturation in the silt and clay size particles. The soil physical fractions were obtained after ultrasonic vibration and sedimentation. Soil organic matter stock (0-20 cm depth) in cultivated sites ranged from 54 to 146 Mg C ha-1 and from 3.9 to 7.1 Mg N ha-1 and in the forest soil 46-94 Mg C ha-1 and 3.7-5.1 Mg N ha-1. The relative distribution of C and N was lower in the coarse sand and highest in the silt fraction; the latter paralleled the changes of whole soil C and N. Coarse sand was the most sensitive fraction responding to cultivation, thus representing a diagnostic pool to assess the SOM shifts under cultivation. Half of our studied soil showed a low degree of C and N saturation in the silt and clay fraction indicating potential for accumulation of C and N, while the other half were well above the saturation limit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".