Using Ground Penetrating Radar to Study Kingston's Community Gardens Over Time
Bibliographic record
Abstract
Ground Penetrating Radar (GPR) is an underground imaging method that sends electromagnetic waves into the ground and creates an image of the subsurface that can be analyzed in a completely non-invasive way. Radar waves are the same as the radiation used in a microwave oven, and the GPR measures the travel time from transmission to return and the amplitude of the waves. The resulting radargram shows how much of the signal is absorbed and reflected in the ground, which is due to the target having different electromagnetic properties than the soil. One of the materials that absorbs the most is water, making GPR one of the most effective methods for imaging groundwater. The city of Kingston is home to many community gardening projects that could benefit from knowing more about the groundwater fluctuations. The three sites selected for this project include the Lakeside Community Garden, the Highway 15 Indigenous Food Sovereignty Garden, and the Rodden Park community garden project. We met and discussed each of the three projects with community project representatives. Across all three communities, the primary goal was consistent: wanting to know how the water moves underground so that they can benefit their planting projects. This led to the primary question of this research study: can GPR work as an effective method to meet the needs of these communities and show differences in the subsurface over the four-month summer period? Six geophysical surveys were conducted including time-lapse GPR, passive seismic, and soil moisture measurements. Data was processed and interpreted for sub-surface features and maps were created as deliverables for the communities. We anticipate that this 4-month project will continue into the future as we just created the baseline for temporal changes of groundwater in Kingston’s community gardens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".