Geophysical and Remote Sensing Characterization to Mitigate McMicken Dam
Bibliographic record
Abstract
By 2002, earth fissures, open ground cracks in the subsurface induced by groundwater withdrawal from basin alluvium, had visibly propagated close to McMicken Dam west of Phoenix, Arizona. Using surface seismic measurements, these fissures were traced to and beyond the dam. Initial test pits and trenches at seismic fissure interpretations confirmed this otherwise undetectable piping erosion hazard at the dam. An investigation to characterize the hazard extent, mechanisms and predict future behavior for risk assessment and mitigation design was then implemented. Deep alluvial basin geometry and material properties characterization across the site was accomplished using surface geophysical gravity, large array surface resistivity and s-wave refraction microtremor seismic methods. Earth fissure presence and absence was further assessed using seismic refraction and test trenches; the results also assisted geotechnical characterization. Newly developed satellite interferometry by synthetic aperture radar (InSAR) provided historic differential subsidence information for the area back to 1992 when data collection began. Finite element modeling developed and calibrated using these results provided predictions for risk assessment and mitigation design. A new dam section avoiding fissures was designed and constructed. The monitoring program includes InSAR and GPS survey, tape extensometers and Time Domain Reflectometry (TDR) at the dam toe.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".