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Record W4389056212 · doi:10.1080/01490451.2023.2286505

Linking Topographical Ring Features to Geochemical and Geophysical Anomalies

2023· article· en· W4389056212 on OpenAlexafffundabout
Konstantin von Gunten, Stewart M. Hamilton, Zach Diloreto, Md. Samrat Alam, Katherine N. Snihur, Maria Dittrich, Kurt O. Konhauser, Daniel S. Alessi

Bibliographic record

VenueGeomicrobiology Journal · 2023
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsThe Scarborough HospitalUniversity of TorontoMinistry of Energy, Northern Development and MinesUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeophysicsGeologyRing (chemistry)Earth scienceChemistry

Abstract

fetched live from OpenAlex

Circular features in forests seen from air have been studied for several decades at different locations around the world. Forest rings, as they are called in Canada’s boreal forests, express several geochemical (pH, carbonate content) and geophysical (surface potential) anomalies on their 20–30 m wide ring edges. Although it has been proposed that microbial processes may cause these anomalies, the exact mechanisms of ring formation are still unknown. We focused on the Thorn North forest ring in Ontario, Canada to correlate the surface potential anomaly to soil gas concentrations. Field measurements showed that the surface potential drop at the ring edge center is framed by peaks in CO2 production, which is linked to O2 depletion and methane generation. Carbon isotope signatures were found to drop to lighter values (down to −20‰), suggesting increased respiration. Higher concentrations of uronic acids bound to extracellular polymeric substances were found, indicating that the surface potential anomaly is linked to respiration. 16S rRNA gene sequencing of shallow soil did not indicate a dominant microbial group on the edges; instead, principal component analysis showed that the microbial composition was controlled by the substrate (clayey vs. sandy soil), therefore future studies should focus on deeper ground layers.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.567

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 science0.0010.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.221
Teacher spread0.212 · 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 designBench or experimental
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
Published2023
Admission routes3
Has abstractyes

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