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Record W4403461883 · doi:10.1139/cgj-2024-0225

Stability of kangaroo rat burrows in the Sonoran Desert: evidence of biocementation

2024· article· en· W4403461883 on OpenAlexvenueno aff
Sera Tirkes, Duygu Yılmaz Aydın, Maura Thompson, Clint E. Collins, Haluk Beyenal, Idil Deniz Akin

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsDesert (philosophy)Geotechnical engineeringStability (learning theory)EcologyGeologyHydrology (agriculture)GeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Kangaroo rats ( Dipodomys deserti) construct complex burrow systems in loose desert sand that survive temperature and relative humidity fluctuations and storms. Animals that burrow in desert sand typically burrow in compacted sand, near plant roots, or when the soil is unsaturated. However, these processes are insufficient to explain tunnel stability of kangaroo rats. Our goal is to understand how kangaroo rat burrows remain stable in loose desert sand, intending to translate this knowledge to geotechnical engineering. A kangaroo rat habitat in the dunes of the Sonoran Desert, AZ, was selected for the study. Dynamic cone penetrometer tests performed at active, abandoned, and no-burrow sites demonstrated that the animals prefer loose sand for burrow construction. Soil samples collected from the burrows’ ceilings, subsurface, and surface were characterized. Brazilian tensile strength test results showed that burrow soil has approximately three times greater tensile strength than the rest at dry state, which indicates increased interparticle attractive stress in burrow ceilings due to biocementation. Laboratory experiments, scanning electron microscopy, and confocal microscopy images showed that fungal and microbial biofilms provided 17 kPa increase in interparticle attractive stress at less than 1% biomass concentration, indicating potential to be used in soil improvement applications.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.258
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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