Grid-based Hydrostratigraphic 3D Modelling of the Quaternary Sequence in the Chateauguay River Watershed, Quebec
Bibliographic record
Abstract
Groundwater recharge, groundwater-surface water interaction, and protection and management of the groundwater resource are strongly constrained by the geological nature of the substratum. A grid-oriented technique has been developed in order to build a 3D stratigraphic model of the Quaternary sediments overlying a regional fractured rock aquifer. The technique is based on the integration of the surficial sediments map and borehole logs with the use of GIS and grid-calculator software Vertical Mapper. The applied methodology focused on estimating the thickness of the stratigraphic units rather than the elevation of the contacts. First, a regular grid was generated over the study area with 30 × 30 m cells. The bulk thickness of the Quaternary sequence for each grid cell was computed as the difference between the terrain digital elevation model and the rock surface krigged over more than 5000 drillers’ logs available. Two computation methods for estimating the discrete thicknesses are discussed and evaluated: the absolute method, in which the thickness of a given unit is computed as an independent value, and the relative method, in which the thickness is computed as a fraction of the bulk thickness. The simplified Quaternary stratigraphy consists, from top to bottom, of: organics (peat), alluvium, lacustrine, aeolian, coarse marine, marine clay and fine silt, fine sandy and silty glacio-fluvial, coarse sandy and gravelly glacio-fluvial, and glacial (silty clayey till) sediments. Their spatial distribution respects fully the surficial sediments map and contacts. At locations with missing stratigraphic data, the borehole logs database was improved with the addition of control points representing the anticipated variation of the successive layers. With the absolute method, derivation of the thicknesses of the layered strata in zones of irregular bedding proved to be difficult. The relative computation method gives more consistent results for various stratigraphic settings and allows rapid, internally consistent estimation of the overburden stratification.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".