Relationship of soil water retention characteristics and soil properties: a case study from the Colombian Andes
Bibliographic record
Abstract
Differences in soil water retention (SWR) characteristics between soil types and the factors driving those differences provide important information for land management, particularly in regions such as the Colombian Andes, which have limited water-storage infrastructure and where soils provide plant-available water and other ecosystem services. The objective of this study was to explore relationships between SWR and physical, chemical, and mineralogical properties of Andisols and Inceptisols through a case study of two watersheds in the Colombian Andes. This study identified a complex relationship between total carbon (TC), short-range order (SRO) minerals, and SWR. Both soil types had high SWR, with volumetric water content at permanent wilting point between 39% and 53%. Principal component analysis showed association of SWR with TC, SRO minerals, and % clay in both soil types. The Andisols of this study were coarse textured, allophanic (rich in allophane and imogolite — up to 17% in the B horizon), and with up to 15% TC in the A horizon. In contrast, the Inceptisols were fine textured (>30% clay) and higher in ferrihydrite than the Andisols. The formation of organo-metallic complexes was observed in A horizons; however, TC was lower under pasture than forest in both soil types. The addition of organic matter to soils with SRO minerals, such as the soils of this study, may foster the formation of organo-metallic complexes, stabilize soil C, and enhance SWR. Consequently, both study sites may benefit from management practices that increase soil organic matter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".