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Record W3026127727 · doi:10.1111/bor.12444

Subfossil oribatid mite communities indicate Holocene permafrost dynamics in Canadian mires

2020· article· en· W3026127727 on OpenAlexfundaboutno aff
Inkeri Markkula, Peter Kuhry

Bibliographic record

VenueBoreas · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsSubfossilMacrofossilPermafrostPeatHoloceneAggradationMireGeologyPhysical geographyRadiocarbon datingSphagnumEcologyArcticEarth sciencePaleontologyOceanographyGeography

Abstract

fetched live from OpenAlex

Permafrost thaw in peatlands is one of the most widespread and worrying consequences of climate warming in the sub‐Arctic area. To predict future climate feedbacks, it is important to study the history of permafrost aggradation and thaw. Plant macrofossil analysis with radiocarbon dating has been widely used in detecting past permafrost dynamics in peatlands, however, due to a lack of permafrost‐specific plant indicator species, determining the exact timing of permafrost aggradation remains a challenge. In this study, we investigated if oribatid mites can be used to determine Holocene permafrost aggradation and degradation in Canadian mires. Based on analyses of subfossil oribatid mite assemblages of Holocene peat profiles from two mires in the Hudson Bay Lowlands area, our results suggest that two species, Carabodes labyrinthicus and Neoribates aurantiacus , are useful bioindicators, which can be used in palaeoecological studies determining permafrost histories. Moreover, our results show that subfossil oribatid mite remains can reveal periods of permafrost, which cannot be determined with certainty based on plant macrofossils alone.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.999

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)0.0020.001

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 head0.025
GPT teacher head0.230
Teacher spread0.205 · 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.

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

Citations9
Published2020
Admission routes2
Has abstractyes

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