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Record W2121822102 · doi:10.4141/cjss09101

Land-use impacts on selected soil properties of the Yungas/Chaco transition forest of Jujuy province, northwestern Argentina: a preliminary study

2010· article· en· W2121822102 on OpenAlexvenueno aff
Shannon Ripley, Gary E. Bradfield, A. A. Bomke

Bibliographic record

VenueCanadian Journal of Soil Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceFirewoodSubtropicsRangelandGrazingForestryLitterDeciduousAgroforestryCanopyPlant litterGeographyEcosystemAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Grazing by domestic livestock, firewood cutting, and timber harvesting are the principal uses of the subtropical transition forest located between the humid Yungas and dry Chaco forests at the base of the Andes mountains in Jujuy province, northwestern Argentina. The objective of this study was to conduct a preliminary comparison of selected soil properties between two common land-use systems - deciduous forest rangeland (DFR) and open savanna anthropogenic rangeland (AR) - in the Yungas/Chaco transition forest. Soil organic C and N were measured at a depth of 0-10 cm, while soil penetration resistance was measured at 0-5 and 5-10 cm depths. Soil degradation in the AR was indicated by lower average values for litter cover (56%), soil organic C (28.1 g kg-1), and total N (2.93 g kg-1), and greater soil penetration resistance compared with the DFR (litter cover 94%; soil organic C 45.0 g kg-1, total N 4.45 g kg-1). This created potential for further soil losses from water erosion during monsoon rains and emphasized the need to establish sustainable grazing management practices.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.188
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes1
Has abstractyes

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