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Record W2804109515 · doi:10.15302/j-laf-20170614

Phantom Ecolog y: Aesthetics, Ethics, and Ecology

2017· article· en· W2804109515 on OpenAlexaffabout
Fionn Byrne

Bibliographic record

VenueLandscape Architecture Frontiers · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLand reclamationLandscape designOil sandsLandscape planningScale (ratio)GeographyEcologyArchaeologyEnvironmental resource managementEnvironmental planningAsphaltEnvironmental scienceCartography

Abstract

fetched live from OpenAlex

Visible from space, the Athabasca bituminous sands region of Northern Alberta, Canada, is currently being engineered at a scale equal to or greater than any other human landscape projects. This massive earth moving operation which at first glance could be confused for a regional urban development, has but one single objective: oil. Yet we do not see landscape architects engaged in the design and specifically the topographic shaping of these sites of extraction, production, waste, and reclamation. Nor are there many examples of landscape architects contributing smaller scale interventions that acknowledge the human occupation of these sites, their experience and material realities. Thus, ten rest stops are proposed to set in the working industrial landscape of the Athabasca bituminous sands, though they might not be built, to emphasize the importance of remembering that these landscapes already exist, showcasing the contemporary Canadian landscape.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.084
Scholarly communication0.0130.003
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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 designTheoretical or conceptual
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
Published2017
Admission routes2
Has abstractyes

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