Využití konduktometru LTC pro charakterizaci proudění vody ve vrtech: Umělý vrt a terénní měření
Bibliographic record
Abstract
My Master thesis is focused on a tracer dilution technique in the well using automatic conductivity logging probes LTC Levelogger (Solinst co. Canada). The main aim of my thesis was to test the application conductivity meters LTC to track the movement of fluids in wells. Different set up were used moving probes with unmodified sensor slit, moving probes with modified sensor slit, probes measuring at fixed points, combined moving and fixed points probes and results were compared. 15 wells in quaternary and 11 wells in Bohemian Cretaceous Basin were measured, some of them repeatedly. The comparison of results indicate that the highest apparent flow velocity have probes with unmodified sensor slit. On the other hand fixed point probes indicate flow velocity, which is 40 - 50% lower at the same wells. The combination of the stable positioned probe LTC and the moving probe LTC has about 40% higher flow velocity than the rate of steady probe LTC placed in the well. The results also indicate that extremely slow velocity values (below approximately 0.02 m/day) can be measured only with LTC probes at fixed points. Modified probe slit was tested in the laboratory in plexi-glass tube using fluorescein and NaCl tracers. Unfortunately the modified geometry of measuring slit does not show distinctively better...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".