Identifying groundwater discharge zones in the Central Mackenzie Valley using remotely sensed optical and thermal imagery
Bibliographic record
Abstract
Landsat 4–5 Thematic Mapper, Landsat 8 Operational Land Imager, and RapidEye-3 data sets were used to identify potential groundwater discharge zones, via icings, in the Central Mackenzie Valley (CMV) of the Northwest Territories. Given that this area is undergoing active shale oil exploration and climatic changes, identification of groundwater discharge zones is of great importance both for pinpointing potential contaminant transport pathways and for characterizing the hydrologic system. Following the work of Morse and Wolfe (2015), a series of image algorithms were applied to imagery for the entire CMV and for the Bogg Creek watershed (a sub watershed of the CMV) for selected years between 2004 and 2017. Icings were statistically examined for all of the selected years to determine whether a significant difference in their spatial occurrence existed. It was concluded that there was a significant difference in the spatial distribution of icings from year to year (α = 0.05), but that there were several places where icings were recurring. During the summer of 2018, these recurrent icings, which are expected to be spring sourced, were verified using a thermal camera aboard a helicopter, as well as in situ measurements of hydraulic gradient, groundwater geochemistry, and electroconductivity. Strong agreement was found between the mapped icings and summer field data, making them ideal field monitoring locations. Furthermore, identifying these discharge points remotely is expected to have drastically reduced the field efforts that would have been required to find them in situ. This work demonstrates the value of remote sensing methods for hydrogeological applications, particularly in remote northern locations.
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".