Bibliographic record
Abstract
Measurement techniques High-precision aerial gravity surveys can be carried out by relative spring meters, with ties to stable reference stations or absolute measurements for time-lapse studies. Instrument drift is controlled by frequent repeat measurement and repeatability of 1-3 µGal has been common. Free-fall gravimeters are heavier and costlier but provide absolute values and are immune to drift. Superconducting gravimeters are stationary and provide sub-µGal resolution over days and weeks, while drift uncertainty can build up to several μGal over years. Cold atom gravimeters are under development and may provide yet another survey alternative in the future. Multiple sensors and multiple repeats are effective ways of improving survey precision, as much of the noise reduce at random noise (sqrt(N)). This holds also for the sensor drift residuals. An efficient, transparent and reproducible processing software is an integral part of such techniques. Surface stations Stability of measurement platforms over years is required for µGal time-lapse precision and can be achieved by installing geodetic monuments. For optimal monitoring of targets like a producing oil, gas or geothermal field, a water reservoir or a volcano, a grid of stations with spacing equal to or smaller than the overburden thickness is required. Surface subsidence or uplift requires sub-cm precision which can be obtained by optical leveling, InSAR or GPS. Accuracy Station repeatability is a robust accuracy measure for relative surveys with multiple occupations of each station. Together with multiple sensors they provide abundant statistics. The redundancy also allows for in-situ calibration of parameters for scale factor, tilt and temperature by minimizing residuals. Time-lapse precision can be judged at stations with minimal or known subsurface changes, and will be affected by gravity survey precision, accuracy of measured depth changes and other time-lapse effects such as benchmark stability and time-lapse signals outside interest. Groundwater variations could be one such noise term, unless the purpose is hydrology monitoring. Efficiency and cost Most microgravity projects have been carried out in a research or development setting, with one sensor, few stations repeat and implicit capital and personnel cost. In a more industrial setting, efficiency is likely to improve, together with reduced survey cost. More instruments and measurements will likely reduce the personnel and mobilization portion of the cost. Precision/cost tradeoffs and value of data will determine the economics of a project, whether in a scientific or commercial setting. Conclusion Currently proven survey repeatabilities of 1-2 µGal may be regarded state-of-the-art and become commonplace for microgravity surveys using relative gravimeters. This can widen the range of applications and reduce monitoring intervals. Further instrument developments may improve this limitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".