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Record W2130394247 · doi:10.5539/jps.v4n1p44

Influence of Stocking Rate on Forage Quality and Diversity in Pyrenean Subalpine Grasslands: Can They Recover if Abandoned?

2015· article· en· W2130394247 on OpenAlexvenueno aff
Rosario Fanlo, Montserrat Bou, Montserrat Ros

Bibliographic record

VenueJournal of Plant Studies · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsEdaphicForageGrazingSpecies richnessMontane ecologyGrasslandEcological successionStockingRangelandNational parkEcologyStocking rateBiodiversityGeographyAbandonment (legal)Environmental scienceAgroforestryBiologyForestrySoil water

Abstract

fetched live from OpenAlex

Can subalpine grasslands recover their pristine conditions if they are abandoned? Since the mid-20th Century, the process of abandonment in European subalpine grasslands due to the decrease in the stocking rate has triggered changes in the forage quality and ecological characteristics of pastures. In this paper we explain the changes (and tendencies) in forage quality and biodiversity in grasslands of Festuca eskia Ramond ex DC from the Aigüestortes National Park (Spanish Pyrenees) that were quasi-abandoned for some years and regained their stocking rate at the beginning of the 21st Century without great changes in climatic and edaphic conditions during the period. Results show a decrease in forage quality (measured as the content of crude protein, phosphorous, fat and pastoral value) and in specific richness at the beginning of abandonment and a regain when the stocking rate was restored. The changes in the variables studied over the period of loss and recovery of grazing show a direct relation to the changes in the functional groups.

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)0.001
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 score0.008
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.135
GPT teacher head0.281
Teacher spread0.147 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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