Barometric pressure responses in groundwater level time series data, a literature review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".