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Record W2504487369 · doi:10.1177/0959683616658527

A 1600-year diatom record of hydroclimate variability from Wolf Lake, New York

2016· article· en· W2504487369 on OpenAlexaff
J. Curt Stager, Brian F. Cumming, Kathleen R. Laird, Alex Garrigan-Piela, Neil Pederson, Brendan Wiltse, Chad S. Lane, Jessica Nester, A. Ruzmaikin

Bibliographic record

VenueThe Holocene · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
FundersLawrence Livermore National LaboratoryUniversity of MinnesotaNational Science Foundation
KeywordsDiatomPrecipitationPeriod (music)CharcoalPaleoclimatologyOceanographyPhysical geographyHolocenePlanktonClimate changeGeologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

A high-resolution diatom record from Wolf Lake, a minimally disturbed ‘heritage’ lake, provides insights into the hydroclimatic history of the Adirondack Mountains of northern New York during the last c. 1600 years. Three pronounced dry periods occurred during c. AD 490–610, 780–870, and 1010–1080, and low precipitation generally prevailed during the warm Medieval Climate Anomaly ( c. AD 950–1350), a finding that fills an important gap in knowledge of the spatial extent of droughts across North America during that period. During the cooler ‘Little Ice Age’ interval ( c. AD 1350–1800), inferred water balance was generally more positive. Seven peaks in charcoal abundance represent fire events during both wet and dry periods. Unusually high charcoal and inorganic sediment deposition c. AD 1700 could reflect human activity in the watershed, as might an abrupt rise in the relative abundances of planktonic and tychoplanktonic diatoms in Wolf Lake during the AD 1860s. The diatom record displays periodicities of c. 256 and 512 years in addition to high-frequency fluctuations, suggesting that significant precipitation variability is likely to continue to disrupt climatic trends in this region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

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)0.0130.002

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2016
Admission routes1
Has abstractyes

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