Geovisualization of boreal peatland architecture in a three dimensional hydrogeological framework using ground penetrating radar and LiDAR at Mariana Lakes, Alberta, Canada
Bibliographic record
Abstract
Communicating science in three-dimensional (3D) multimedia is an immersive and interactive way to explore scientific processes (Signals and Communication Technology, 2019). Geovisualization is an emerging 3D multimedia method for visual analysis, synthesis, and presentation of geospatial, geologic, and geophysical data (MacEachren & Kraak, 2001). There is an identified need to develop scientific communication tools to further understand boreal peatland evolution, hydrogeology, ecology, and geochemistry (Bubier et al., 2003) since the International Union of Conservation of Nature asserts that peatlands are among the most valuable ecosystems on Earth, critical for preserving global biodiversity, providing drinking water, minimising flood risk, preventing wildfire, and mitigating climate change (Hama et al., 2000). The intention of this thesis is to communicate a novel approach to geovisualize boreal peatland architecture using Light Detection and Ranging (LiDAR) and Ground Penetrating Radar (GPR). GPR and LiDAR have been used to create 3D subsurface geovisualizations for archaeology (Kenady et al., 2018; Schultz & Martin, 2011) and resource geology (Corradini et al., 2020; Koyan & Tronicke, 2020) although application to peatland hydrogeology is uncommon. Point-source hydrogeological and geochemical data were integrated with 3D geological models to estimate carbon and nitrogen storage in an archetypal boreal peatland near Mariana Lakes, Alberta. Peatland geometry resembled a shallow lake basin with depths greatest in fens (>10 m) and thinnest in bogs (<2 m). Hydraulic conductivity was only a few meters per year and vertical groundwater movement was limited. Sequestered carbon and nutrients increased with depth. The average concentration of dissolved ammonium was 3 grams per cubic metre of peat (g/m3), 5g/m3of Total Kjeldahl Nitrogen, 60g/m3 of dissolved organic carbon and 200g/m3 of dissolved inorganic carbon. Tritium detection from atmospheric atomic weapons radionuclide fallout revealed that in deeper anaerobic peat (catotelm), tritium was absent, signifying groundwater was older than 50 years and not mixed with meteoric waters. Fen catotelm channels are likely acting as gravity-driven hydraulic traps (Tóth, 1999).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".