Aggregated gridded soil texture dataset for Mackenzie and Nelson-Churchill River Basins.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The dataset contains 0.125 resolution gridded soil texture data for Mackenzie and Nelson-Churchill River Basins. The data has been aggregated from two source datasets, namely Soil Landscapes of Canada 2.2 dataset and STATSGO2 dataset for USA. The final texture data has the minimum, maximum and average percent for sand, clay and organic components of soil per grid cell of the two basins. This data set is particularly created for the use with MESH (Modélisation Environnementale communautaire - Surface Hydrology), a distributed coupled hydrology-land surface model developed by Environment and Climate Change Canada, as applied to Mackenzie and Nelson-Churchill River Basins. It can be used for similar applications of land surface, hydrology, or environmental models as appropriate.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it