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Record W3087773159 · doi:10.12911/22998993/126875

Micro-Climatic Amelioration in a California Desert: Artificial Shelter Versus Shrub Canopy

2020· article· en· W3087773159 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Ecological Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsYork University
FundersU.S. Bureau of Land ManagementNatural Sciences and Engineering Research Council of CanadaMitacsYork University
KeywordsShrubCanopyDesert (philosophy)Environmental scienceAgroforestryEcologyForestryGeographyBiology

Abstract

fetched live from OpenAlex

Anthropogenic factors such as climate change, land use, urbanization, alongside the spread of invasive species are some of the challenges impacting the arid and semi-arid regions globally. The canopy of many native plants including shrubs and trees not only provides refuge from predators for some animals but also offers a shelter from climatic stressors for other plants. The canopy of native vegetation can thus be a microhabitat critical to the persistence of many species locally, and it is vital to better understand its importance for the conservation and recovery of species in these landscapes. In this study, we tested the hypothesis that triangular and rectangular artificial canopies function similarly to the canopy of resident native shrubs when ameliorating the understory micro-climate. Three light permeabilities including 15%, 50%, and 90% were tested by measuring soil and air temperature with light relative to paired open gap (non-canopied) microsites and shrubs. Shelters offered more stable temperatures and reduction in light compared to the open gap and were not significantly different from established native shrubs. This suggests that this simple, affordable intervention can provide a stop-gap solution that approximates natural heterogeneity in climate at fine scales and offers a refuge whilst managers and stakeholders restore native vegetation such as slow-growing and difficult to establish shrubs within this ecosystem.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.341

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)0.0000.000

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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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