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Record W6961103656 · doi:10.14288/1.0448736

The Moss Way : Bio-adaptive reuse through urban mosses

2025· article· en· W6961103656 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMossReuseAdaptabilityReciprocity (cultural anthropology)NoticeDemolition

Abstract

fetched live from OpenAlex

Once you notice moss, it suddenly seems to appear everywhere. Mosses are among the most widespread and abundant life forms on Earth. With over 12,000 species, these tiny, ancient plants are a testament to the diversity and adaptability of life. Mosses encourage us to slow down and be more attentive to the smaller wonders of our world. Their fascinating roles underscore principles of reciprocity and mutual support, concepts that are increasingly vital in the practice of landscape architecture. This project works at the timescales of moss to learn from its material and ecological potential. With the future demolition of the Vancouver Aquatic Centre as a case study, The Moss Way proposes bio-adaptive reuse as a strategy for cultivating, sustaining, and celebrating this little giant in our urban landscapes.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.159
Teacher spread0.149 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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