Near‐Surface Geophysics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
Bibliographic record
Abstract
Abstract Pointing to the Integrated, Coordinated, Open, Networked Findability, Accessibility, Interoperability, and Reusability (ICON‐FAIR) principles, we have determined several opportunities for implementation within the realm of near‐surface geophysics (NSG), representing a broad range of data acquisition and processing technologies. Our work explores the multifaceted community‐driven nature of NSG and, by applying ICON‐FAIR principles, we identify three key strategic objectives: (i) the development of an approach to integrating NSG into other geoscience data collection projects, (ii) the creation of coordinated and open standardized NSG data, and (iii) the networking of post‐secondary institutions to foster an equipment sharing consortium. The precedence within the geoscientific community demonstrates that there are significant opportunities for advancing interdisciplinary applications of NSG through the implementation of structural change within the ICON‐FAIR framework.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".