A Method For Integrating Geophysical Information Management
Bibliographic record
Abstract
The Geological Survey of Canada at Bedford Institute of Oceanography (BIO) has initiated a project to enhance its environmental marine geology program with the purchase of modem digital data collection and processing equipment. The success of the project depends upon the degree to which new methods can be integrated with traditional data management practices. Late in 1990 the Geological Survey of Canada initiated the Digital Initiative project to enhance its environmental marine geology programme with the purchase of modern geophysical data collection and processing equipment, and the development of related data management tools. A six person team is responsible for planning and implementing systems appropriate to the research objectives of sixty people. Equipment to be acquired over the four year life of the project comprises loggers to digitize and store data, a towed body positioning system, interferometric swath bathysidescan, high resolution multichannel seismic streamer designed to deploy from a deep tow body, a T1 protocol telemetry system for the latter and attendant computer facilities. Computer resources were augmented in the second year of the project with the purchase of a Hewlett-Packard model 750 (HP 750) server, five HP 720 workstations, three HP 710 workstations, twenty gigabytes of disk storage and three 2.3 gigabyte tape storage devices. Source code obtained from third party sources for seismic and sonar processing applications is being tailored to meet system requirements. Additional software is being written in house to integrate these and other third party applications (including a Geographic Information System (GIs), a publishing package, time series and image processing programs) under a common Graphical User Interface (GUI). All development is being done in the C language using X Windows and Motifm where appropriate, in a UNIX environment. Use is made of public domain source code where possible. Technology enhancement provides opportunity: it does not guarantee increased productivity because it introduces change. The challenge facing the Digital Initiative (DI) is not simply systems implementation, but also timely delivery of the benefits. Care is being taken to ensure that newly acquired hardware and software tools are appropriate and adequate for future research programs. This paper outlines the approach being taken toward software systems integration and discusses some of the design elements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".