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Record W2182288094 · doi:10.5822/978-1-61091-211-2_12

Edible Landscaping and Xeriscaping

2012· book-chapter· en· W2182288094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIsland Press/Center for Resource Economics eBooks · 2012
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsLandscapingResource (disambiguation)BusinessAesthetic valueEnvironmental planningArchitectural engineeringConsumption (sociology)Value (mathematics)Environmental scienceEnvironmental economicsNatural resource economicsEnvironmental resource managementEnvironmental engineeringEngineeringEnvironmental protectionComputer scienceSociologyEcologyEconomicsAestheticsSocial scienceBiologyArt

Abstract

fetched live from OpenAlex

Conventional landscaping with grass, shrubs, and trees requires excessive amounts of water, maintenance, and chemicals, which result in resource depletion, energy consumption, pollution, and contamination. Because of a growing public and professional awareness, alternative practices such as edible landscaping and xeriscaping are taking hold in residences. These alternatives can significantly reduce adverse effects on the environment while maintaining aesthetic value. The principles behind these methods and their applications are discussed in this chapter.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.317
Threshold uncertainty score1.000

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)0.0010.000

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 head0.026
GPT teacher head0.215
Teacher spread0.189 · 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