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Record W4291335376 · doi:10.1016/j.dib.2022.108541

∼14 000 years of geochemical and isotopic data from Lake Simcoe, Canada

2022· article· en· W4291335376 on OpenAlexafffundabout
Rebecca M. Doyle, N. Bumstead, C F M Lewis, Fred J. Longstaffe

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsBedford Institute of OceanographyWestern University
FundersNatural Resources CanadaCanada Research Chairs
KeywordsDeglaciationδ18OMeltwaterIce coreIsotopes of oxygenGeologyδ13COceanographyIsotopes of carbonRadiocarbon datingPhysical geographyGlacial periodStable isotope ratioHydrology (agriculture)PaleontologyTotal organic carbonGeochemistryEnvironmental chemistryChemistryGeography

Abstract

fetched live from OpenAlex

This dataset contains measurements of modern water and ancient core materials from Lake Simcoe, the fourth largest lake wholly in Ontario, Canada. These data consist of: (i) oxygen, hydrogen and carbon isotope (δ18O, δ2H and δ13C) compositions for modern water samples; (ii) physical measurements of one piston core, PC-5; (iii) δ13C and δ18O values of ostracods collected from PC-5, and (iv) δ13C and δ18O values of ancient DIC and water, respectively, inferred from item (iii). Physical measurements performed on core PC-5 include magnetic susceptibility, mineralogy and grain size. Mass accumulation rates are also reported. These data will be of interest to those aiming to better characterize the timing and pathway of meltwater flow during and following deglaciation of the Laurentide Ice Sheet in the Laurentian Great Lakes region. These data will also be useful to researchers investigating the influence of deglaciation on the oxygen and carbon isotope systematics of ancient lake environments. A discussion of these data is available in “A ∼14 000-year record of environmental change from Lake Simcoe, Canada” [1].

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 score0.342
Threshold uncertainty score0.994

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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