Digital Soil Mapping and Morphogenetic Characterization of Soils of Ebonyi Formation in South Eastern Nigeria
Bibliographic record
Abstract
Soils formed from Ebonyi formation in south eastern Nigeria were mapped and classified for effective management and sustainable land use. The soil mapping was executed by combination of conventional method, soil landscape relationship and spatial analyses in GIS environ using digital terrain model to produce the soil map of Ikwo Local Government in Ebonyi State at a scale of 1:50,000. The soils were characterized and classified for effective soil management based on results of analyses of soils sampled from profile pits. The results show four mapping units based on the geology which comprises mainly sandstone, silt stone, shale and river alluvium parent materials. The major soils mapped include Typic Udifluvent, Aquertic Udifluvent, Typic dystrudept and Ultic Udifluvent. The morphology shows influence of low Ca:Mg ratio and high clay content in the subsoil (>30%) consequent ponding and hydromorphic soils. Soil quality is low to moderate based on pH and of between 4.14 to 5.19 which decreased down the profile. This also explains decrease of base saturation in some pedons. (16.84 and 98.17%). The acidic and anaerobic condition of some of the profiles must have aided the Fe/Mn movement to form Fe/Mn nodules at the base of some of the profiles. Bi genetic processes was involved in the lowlands including cummulization and gleization with formation of alluvium at the upper horizons with little stratification of the underlain parent materials due to combination of ecto/endo saturation. Other processes include Ferruginisation at break of slope and in situ formation of soils with high clay and silt content in areas with geohydrology setting of marine sedimentation of marl. The combination of poorly bedded shales, siltstones and limestone highly influenced formation of multifaceted terrain with rolling to undulating uplands and flood plains at the lowlands.
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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 | 0.002 | 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) | 0.001 | 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".