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Record W2110991560 · doi:10.5424/fs/2014231-03674

Soil development mediated by traditional practices shape the stand structure of Spanish juniper woodland

2014· article· en· W2110991560 on OpenAlexaff
Javier De la Fuente-Leon, Edgar Lafuente-Jimenez, Daphne Hermosilla, M. Broto, Antonio Gascó

Bibliographic record

VenueForest Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsCanadian Association for the Study of the Liver
FundersU.S. Forest Service
KeywordsWoodlandJuniperLivestockForestryBasal areaSoil waterAgroforestryStock (firearms)Environmental scienceGeographyEcologyBiologyArchaeologySoil science

Abstract

fetched live from OpenAlex

Aim of study: Assessing the effect of soil development on the stand structure of a Spanish juniper forest traditionally shaped by livestock browsing and wood extraction. Area of study: Berlanga de Duero (Soria, Castilla y León), Spain.Material and Methods: A stand inventory served to record stand structure. Tree age, height, DBH, basal area, and overbark volume were determined in each plot. Results were pooled considering two well-differentiated degrees of soil evolution. One-way ANOVAs (and Tukey’s test) and regressions between growth parameters were performed to assess significant differences between growth performances on both types of soils. Research highlights: Deeper soils yielded significant higher plant density and stand stock figures than stony shallower profiles despite the intense past livestock activity in the area; and single tree-size was also significantly greater. Non-significant differences were found for merchantable junipers age (≈120-160 years). Wood extraction and livestock browsing should be limited on shallower soils to allow soil and forest evolution; as well as to preserve the genetic pool better adapted to hardest growing conditions.Keywords: livestock browsing; forest development; Juniperus thurifera L.; soil evolution; stand stock.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.187
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.201
Teacher spread0.182 · 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 teacher head, 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

Citations0
Published2014
Admission routes1
Has abstractyes

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