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Record W6995790783

Prairie terroir - designing for sustainable viticulture

2018· dissertation· en· W6995790783 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsViticultureTerroirPracticumAgricultureClimate changeVineyardAgricultural productivitySustainable agriculture
DOInot available

Abstract

fetched live from OpenAlex

This practicum examines the feasibility of cold-climate viticulture in southern Manitoba in light of ongoing climate change and establishes principles for sustainable viticulture appropriate to Manitoba’s geological conditions. As the climate continues to change, drastic changes will begin to occur within the wine industry. Regions that are currently suitable for production are likely to be subject to higher temperatures, more erratic precipitation and harsher weather. An opportunity exists within the Mowbray Plateau located in the Pembina River Valley, to establish a new type of agriculture in the Prairies—one that can grow and adapt as the climate changes. Prairie viticulture is feasible if done appropriately, utilizing sustainable landscape strategies such as planned cover crops, creating smaller microclimates, and planting according to terroir. These strategies can evolve alongside climate and microclimatic-specific research. This practicum demonstrates through case studies of existing cold-climate grape growers, and by research and design, that the Prairies could provide a healthy future for sustainable viticulture.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: Other design
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.025
GPT teacher head0.240
Teacher spread0.216 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

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