The use and application of GPR in sandy fluvial environments: methodological considerations
Bibliographic record
Abstract
Abstract Ground penetrating radar (GPR) is a popular technique for imaging and interpreting sedimentary architecture. However, current literature shows a wide range in the quality of information provided on the GPR methodology and processing technique. It is therefore difficult to judge the validity of the GPR interpretations and this produces inherent difficulties for comparison between surveys. This paper describes the key steps required to collect, process and interpret GPR surveys in sandy fluvial sediments. GPR data from the South Saskatchewan River, Canada, are used to illustrate each stage of data collection and processing. Particular attention is given to the appropriate set-up conditions for the GPR software and hardware, the selection of data-processing techniques and velocity analysis. Methods for the interpretation of GPR reflectors are also investigated using ground-truth control provided by a cut-face exposure. This paper presents recommendations for a systematic and rigorous methodology for the collection, processing and interpretation of GPR data in sandy fluvial environments. The paper suggests that all data-collection parameters and processing steps should be recorded or tabulated in any GPR publication to facilitate comparisons between surveys.
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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 | 0.098 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".