An animation of the 3D Phanerozoic geological model of southern Ontario
Bibliographic record
Abstract
A preliminary 3D model of the Paleozoic bedrock geology of southern Ontario has been constructed using Leapfrog implicit modelling software, subsurface geological data and expert knowledge. With advances in computer hardware and software, and availability of digital well and drillhole databases it is now possible to model and visualize subsurface geological relationships in 3D at regional scales. This is a valuable tool for geologists in interpreting and understanding the geology and geological history of an area, and for communication of geological concepts to non-geologists. In the virtual visualization environment, the geology can be examined from a number of perspectives interactively. The stratigraphic succession and boundary geometry can be identified by either progressive removal of units or cross-section slicing. In the southern Ontario model features that can be viewed and studied include depositional and erosional limits, reefs, faults, salt dissolution and collapse structures, regional dips, arches, depositional and structural basins, oil and gas traps, and regional aquifers. To increase the visibility of this model and to expand the audience beyond the technical geological client group an animation of the model has been produced. The animation is approximately three minute and thirty seconds long and provides a systematic progression through the model units, provides regional context, an overview to the data support, and illustration and explanation of geological features. Selected geological features are presented and highlighted through graphic techniques supported by embedded imagery, annotations, animations and maps. It previews the shareware viewing software available for viewing of the model and highlights some of the tools available for interacting with the model. Communication of geoscience knowledge to audiences outside of the core geoscience community is key to support groundwater related decision-making. The animation has been released on GEOSCAN; however, is inadequate unless publicized through other mechanisms, public awareness of GSC publication released via Geoscan is limited. To enhance publication visibility the mp4 file was posted on YouTube, LinkedIn, and ResearchGate. In this case, LinkedIn proved to be the most successful in reaching an expanded audience. Within one week of posting the animation was viewed by over 800 people, reaching over 3 times the number of LinkedIn connections attributed to the author. LinkedIn provided summary information by country, title (geologist), and company affiliation. Interest in the model was focused in Ontario; however; significant access to the model also occurred in Vancouver and Perth Australia. Based on company affiliation access was logged from a suite of recognized hydrogeological consultants working in Canada, 3D modelling companies in New Zealand, and provincial agencies, e.g. the Alberta Energy Regulator. Penetration within YouTube (32) and ResearchGate (5, 2 days) was one to two orders of magnitude less than via LinkedIn. Additional social media options such as Mendeley, Facebook, and Twitter were not exploited but likely would provide exposure, at least in part, to complementary audiences rather than targeting the same audiences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".