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Record W4362475804 · doi:10.24908/iqurcp16347

Soil Properties of Abandoned Vineyards

2023· article· en· W4362475804 on OpenAlexaffvenue
Mackensie Dodd

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsVineyardEnvironmental scienceLand coverLand useAbandonment (legal)GeographyAgroforestryForestryHydrology (agriculture)GeologyArchaeologyEcology

Abstract

fetched live from OpenAlex

The abandonment of vineyards has shown to change the soil properties and structure. The analysis of vineyards in the Castilla y Leon region in northern Spain and the soil properties of specific points in the regions demonstrate that there is a change after abandonment of vineyard land. Historical data has shown that there has been observed changes in the soil and the hydrological properties in the soil after a vineyard has been abandoned (Vazquez-Blanco, 2022). Comparisons made between active vineyards and those that have been abandoned show some contrasts between each in the collected soil data and how the land use and land cover has changed (ESDAC, 2022). Additionally, the determination of why the vineyards were abandoned by the production yield and if the vineyards were abandoned due to economic reasons or if climatic changes caused for the land to be deem unsuitable for vineyard occupation. The purpose of this report is to show that soil properties and soil structure is altered in an area after vineyards are abandoned and the land is no longer cultivated. The comparisons of active vineyards and vineyards that are now abandoned provide for the stark differences to be observed.

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)0.001
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.193
GPT teacher head0.357
Teacher spread0.164 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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