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Correlation of seasonal precipitation isotopic profile with the modern climatological data: a case study from the western Newfoundland region of Canada

2020· article· en· W3087518362 on OpenAlexaffabout
Brittany Marche, Harunur Rashid, Don-Roger Parkinson

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPrecipitationClimatologyEnvironmental scienceSeasonalityOceanographyPhysical geographyGeographyGeologyMeteorologyStatistics

Abstract

fetched live from OpenAlex

The measurement of stable water isotopes (δ<sup>18</sup>O and δ<sup>2</sup>H) in precipitation is a powerful tool for detecting changes in climate patterns, groundwater movements, and hydrological budget. In this study, daily precipitation was collected and δ<sup>18</sup>O and δ<sup>2</sup>H were analyzed in Corner Brook, western Newfoundland for 2015. The study provides the first background data of any kind related to water isotopes in western Newfoundland. 134 samples were analyzed using the Picarro Liquid Water Isotope Analyzer L2130-i, with a minimal instrumental error. The data suggest seasonal variations in which the δ<sup>18</sup>O varies from -33.4 to -0.03 ‰ (±0.023 ‰) and δ<sup>2</sup>H ranges from -253.4 to 15.1 ‰ (±0.148 ‰). Our data are compared with modern meteorological data and publicly available δ<sup>18</sup>O and δ<sup>2</sup>H data from greater Atlantic Canada, which suggests that the atmospheric circulation patterns, spatial features, and other climate factors are distinct in Corner Brook. Isotopes in meteorological precipitation data referenced and collected in this study reflect the cool, wet climate and air-mass fluctuations unique to the geographical region and thus, this baseline is fundamental to understanding the modern isotope hydrological/climatic studies for 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)0.000
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 score0.486
Threshold uncertainty score0.995

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)0.0050.000

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 head0.057
GPT teacher head0.226
Teacher spread0.169 · 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
Published2020
Admission routes2
Has abstractyes

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