Time‐Elevation Head Sections: Improved Visualization of Data from Multilevels
Bibliographic record
Abstract
Abstract Hydrogeologic investigations of fractured rock are evolving toward increasing spatial and temporal resolution with increasing use of multilevel systems with 10 or more intervals in a single borehole, each with auto‐sampling sensors monitoring pressure, temperature or chemistry for weeks or months, creating large quantities of densely sampled data (time and space). These data are typically displayed as hydrographs for analysis of site‐specific controls on groundwater flow. We present a method for presentation of high density pressure head data from multilevel installations referred to as time‐elevation head ( TEH ) sections that improves visualization of spatial and temporal responses of the hydrogeologic system to external stresses. Data collected from two multilevel installations, each with 13 functioning pressure transducers monitoring the upper 40 m of a dolostone aquifer, over a period of 83 d, prior to, during and after a pumping test are used to present TEH sections and examples of data processing. TEH sections are produced using commercially available software designed for geophysical data collected at closely spaced intervals along sub‐parallel lines. These algorithms perform calculations orthogonally either in time (“ X ” axis) or elevation (“ Y ” axis) to interpolate a regular grid of head and subsequently when filtering is used to identify subtle trends within the data. The base and filtered TEH sections are used to interpret response of the system to transients and infer hydrogeologic characteristics of the site. The utility of the process is dependent on the precision and accuracy of the head data as well as an informed user to avoid introducing spurious features into the sections.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".