Profiling of Aeromagnetic Data Interpretation using the Eye Tracker
Bibliographic record
Abstract
SummaryInterpretation of geoscientific data is a difficult and sometimes an impossible task. The interpretation process involves the interplay between what are obviously or objectively noticeable phenomena in observations and what interpreters bring (i.e. subjectivity) to the interpretation in regards to presuppositions and expectations. As a first step towards understanding subjectivity and human biases in interpretation, our study focuses on quantitative profiling of the behaviour of interpreters. Whilst the data is being observed, we use an eye tracker that captures eye gaze information associated with the visualization of the data. The specific aims of this study include the analysis of: (1) target spotting accuracy and efficiency between interpreters with different levels of experience and different geoscience expertise; (2) the impact of commonly used enhancement tools for data interpretation. A preliminary experiment was conducted using an aeromagnetic dataset from Ontario, Canada, and the 1st order vertical derivative (1VD) of the corresponding data, to characterise the observation patterns in free viewing and target spotting accuracy for specific geological features (faults, granitoid intrusions, kimberlite pipes). The results showed: some distinct observation patterns between experienced and inexperienced interpreters; and the impact of the 1VD enhanced data in interpretation. The study outcomes may impact on a wide range of geoscientific activities including: risk management in decision making for the mining industry; education and training of geoscientists; and the development of enhancement tools for geoscientific data.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".