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Record W3006175078 · doi:10.1080/15230430.2020.1712858

Soil conditions required for reaction wood formation of drunken trees in a continuous permafrost region

2020· article· en· W3006175078 on OpenAlexaboutno aff
Kazumichi Fujii, Koh Yasue, Yojiro Matsuura, Akira Osawa

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsPermafrostTable (database)Soil waterBlack spruceTree lineEnvironmental scienceGeologyWater tableTaigaForestrySoil scienceGeographyClimate changeGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

Black spruce trees lean to form “drunken” forest on degrading permafrost; however, the causes of tree leaning on continuous permafrost remain unclear. Leaning events are recorded by reaction wood formation in tree rings, and it remains unclear what soil conditions are required for reaction wood formation of drunken trees. Tree disk morphology and soil hummock properties were examined for fifty tree–mound combinations in Northwest Territories, Canada. Spruce trees growing on mound edges form reaction wood on the downslope sides of their trunks. Reaction wood formation in mature trees was greatest in stem tissues between ground level and 30 cm aboveground. Reaction wood formation occurred only in trees growing on mound edges. The extent of reaction wood formation was higher in trees growing in clayey soils than in trees on sandy soils. For trees growing on clayey mound edges, the extent of reaction wood formation decreased with increasing permafrost table depth. Black spruce tree rings formed between ground level and 30 cm aboveground could record movement of clayey soil hummocks over shallow, underlying permafrost tables. A combination of clayey soil texture and shallow permafrost table is likely required for development of hummocks and drunken forests on the continuous permafrost region studied.

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 score0.068
Threshold uncertainty score0.998

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)0.0000.000

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 head0.115
GPT teacher head0.325
Teacher spread0.210 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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