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Record W2030066141 · doi:10.2166/nh.2008.003

Hydroclimatic analysis of an ice-scar tree-ring chronology of a high-boreal lake in Northern Québec, Canada

2008· article· en· W2030066141 on OpenAlexafffundabout
Mickaël Lemay, Yves Bégin

Bibliographic record

VenueHydrology research · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesArcticNet
KeywordsFlood mythDendrochronologyClimatologyPhysical geographyPrecipitationChronologyShoreGeologyEnvironmental scienceGeographyOceanographyMeteorologyArchaeologyPaleontology

Abstract

fetched live from OpenAlex

Ice-scars were investigated to reconstruct the ice-flood history of Corvette Lake in Northern Québec in an attempt to determine the hydrological threshold of shore ice and identify the accompanying climatic conditions. The ice-scar record started around 1850 and showed a rapid increase in frequency at the start of the 1930s, while trees damaged by the ice were already mature and established over several decades. The study supports the hypothesis that this shift could correspond to an increase in flood discharge. Hydrological analysis of every event that occurred since 1961, the year during which instrumental recording began, indicated that scar frequency and scar maximum height were strongly correlated with average recession discharge, average flood discharge, peak discharge and flood onset. Ice-scars provided a discontinuous record of discrete events triggered by hydrologic extremes that were used to document the instrumental record using logistic regression. Results from multiple regressions suggested that ice-scars correspond to years with highs in total precipitation from January to March and from May to June, in the sum of degree-days of frost in April, and in the sum of degree-days of heat from October to April. Although imperfect for reconstructing past events, this study exemplifies the potential use of ice-scars for extending the historical record of ice-floods with hydroclimatic significance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.040
GPT teacher head0.270
Teacher spread0.230 · 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

Citations13
Published2008
Admission routes3
Has abstractyes

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