Near surface seismic database: future online GSC data delivery
Bibliographic record
Abstract
The near surface geophysics group at the Geological Survey of Canada (GSC) has developed a land streamer system for the rapid acquisitions of seismic data. With this system, over 700km of near surface seismic data has been collected over the last decade, and the data collection is rapidly growing. Using Microsoft Access a database has been designed to catalogue these projects and the associated processed seismic sections, with ease of upkeep as a priority. To accomplish this, a library of Access Macros was created to make data entry as efficient as possible. With the macros working in the background, a new survey is added to the database in a few minutes. When a project is entered, the associated files are automatically copied into a backend file structure, creating a consistent and organized backup of the processed seismic lines, their coordinate files, and the log sheets. Macros automatically generate and store links to these files in the database. Storing the archived data as links rather than attachments keeps the database below the file size limit of 2GB without losing file accessibility. Additionally, the relevant spatial meta-data (such as survey boundaries in UTM and Latitude-Longitude coordinates) are extracted automatically, instead of having the user sift through and manually enter this data. Additional meta-data is entered manually, such as survey description, acquisition and processing parameters. Meta-data is important as it provides the user with critical information that supports analysis and interpretation of the survey results. The GSC is committed to the ISO 19115 metadata standard and all published data has to be fully compliant with the Federal Geospatial Platform Harmonized North America Profile (FGP HNAP) metadata profile (http://www.nrcan.gc.ca/earth-sciences/geomatics/canadas-spatial-data-infrastructure/geospatial-communities/federal). Survey coordinates can be exported from the database as KML files, making them compatible with Google Earth. This is useful for quality control and spatial querying as eventually this database will form the backbone of an internet accessible tool to search, view and download near surface seismic profiles in SEGY format, which is a proprietary SEG industry standard. It will be much easier to access data from a standardized structure at one location rather than independent Open Files. This is expected to help applied geo-scientists and researchers integrate this information into their work. This project aligns with the Government of Canada Open Data policy and delivery within a searchable online mechanism (http://open.canada.ca/en/open-data).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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; both teacher heads agree on what is shown here.
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".