On the Method of temperature Measurements in Borehole Using Precision thermometers
Bibliographic record
Abstract
Precise autonomous borehole thermometers (loggers) allow measuring temperature anomalies of small amplitude (up to 1 mK). It significantly expands the range of problems that borehole thermometry can solve in exploration geophysics, hydrogeology, engineering geology, and geoecology. However, thermal logging techniques remain outdated that hinders the effective implementation of the capabilities of these devices. The paper discusses methodological issues of precision temperature measurements in water-filled boreholes. Experimental studies with an autonomous thermometer rBrsolo³ T (RBR Ltd., Canada) showed that the device’s response to changes in ambient temperature (relaxation) is complex. Within a few seconds after the immersing of device in a borehole, it registers a temperature close to the fluid temperature. However, this temporary “window” closes soon, and after 15–20 seconds the measured temperature begins to deviate more and more from the undisturbed temperature. Next time measured temperature returns to the undisturbed temperature after 1000–6000 sec. In addition, the temperature response in the interval of 20–600 sec is complicated by non-periodic fluctuations with an amplitude of 0.02–0.05 K associated with thermal convection. The presence of a time “window” on the relaxation curve makes it possible to measure fluid temperature quickly and accurately in stop-and-go mode. The design parameters of thermometers determining the width of the “window” were assessed using mathematical modeling. Recommendations for conducting precision temperature logging of boreholes in both continuous and stop-and-go modes have been provided.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".