Strategies for Accurate Determination of Drift Characteristics of Unstable Gravimeter in Tropical, Coastal Environment
Bibliographic record
Abstract
The drift characteristics specific to an unstable gravimeter has been modeled to enhance high quality data that will be useful for gravimetric studies and to determine proper timing of field observations. A pre-field observation was carried out to monitor the tide and thus limiting the relative gravity observation to near-linear time window. Closed loop sequence technique of re-occupying a drift base compatible with the drift characteristics of the Lacoste and Romberg (model G446) and cascade model for the computation of drift were combined to obtain a more reliable data that fulfills the linear drift assumption. Subjecting the modelled drift to descriptive statistics a maximum value (1.6550mGal) and minimum value (- 0.3720mGal) of drift were obtained. This variability in drift values and the disparity between the mean (0.099mGal) and the standard deviation (0.2914mGal) is a pointer to various factors that caused the instrumental drift. Such factors could be attributed to external temperature, age and usage of the gravimeter, mechanical stress and strain in the mechanism as the gravimeter is moved and subjected to vibrations. The low standard error of the mean (0.0196mGal) is a reflection of the validity of the linear drift assumption using the cascade model and the field procedure compatible with the drift characteristics of the gravimeter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".