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Record W2981946410 · doi:10.4095/299783

Barometric pressure responses in groundwater level time series data, a literature review

2017· review· en· W2981946410 on OpenAlexaboutno aff
C Milloy

Bibliographic record

Venuenot available
Typereview
Languageen
FieldComputer Science
TopicTime Series Analysis and Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSeries (stratigraphy)Atmospheric pressureGroundwaterHydrology (agriculture)MeteorologyGeographyGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Ontario's Ministry of Natural Resources and Forestry (MNRF) oversees Ontario's Low Water Response (OLWR) Program. As partners with MNRF, conservation authorities across Ontario coordinate responses to local low water conditions, amongst the various levels of government, as conditions arise. In further support of OLWR, conservation authority (CA) geoscientists have, in the past, participated in a technical consultation group with staff from MNRF and from the Ministry of the Environment and Climate Change (MOECC). The technical geoscience group convenes once or twice annually to discuss amongst other topics, the potential uses of available groundwater level monitoring data from the MOECC's Provincial Groundwater Monitoring Network (PGMN) in the OLWR Program. In support of the use of this data, the staff geoscientist from the Rideau Valley Conservation Authority (RVCA) was asked to review and report on the available literature that explains the barometric responses, which are known to be recorded in some groundwater level monitoring data-sets. This presentation will: provide an overview of the information summarized in the literature review; present several examples of barometric pressure responses and earth tide effects in PGMN time-series data; explain the necessary next-steps in the initiative; and summarize the relevance for all groundwater level monitoring projects.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0070.004
Research integrity0.0000.001
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 head0.190
GPT teacher head0.368
Teacher spread0.178 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2017
Admission routes1
Has abstractyes

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