Peat depth and carbon storage of the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
This dataset contains ground-measured peat depth and maps with the spatial distribution of peat depth, carbon stock and uncertainty in the Hudson Bay Lowlands, Ontario, Canada. The ground-measured peat depth data was collected by a Russian-type peat corer from 32 sites in 3 groups spanning from 51oN to 55oN in July and September 2022. The version 1 maps were produced in the Remote Sensing Lab, McMaster University, on March 2024. To generate the peat depth map, we used 495 peat depth records from Ontario Ministry of Natural Resources and Forestry data archive, topographic information, long-term satellite observations of land surface temperature, greenness, and polarization signatures in Synthetic-Aperture Radar (SAR) as well as machine learning models. Data was trained using multi machine learning algorithms. Based on the Root Mean Squared Error (RMSE) derived from 10-fold cross-validation, four models were selected for further prediction, including Gradient Boosting Machine (GBM), Deep Learning, Distributed Random Forest (DRF) and Extreme Gradient Boosting (XGBoost). For the final peat depth map, a second-level “meta-learner’ called stacked regression was applied to find an optimal combination of the 4 base models. Here we used generalized linear model (GLM) during the stacking process to map peat depth for the entire HBL. The uncertainty was estimated as ± one standard deviation around the mean estimates of all base models. The carbon stock map was estimated based on empirical relationship between the estimated peat depth and C stock.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".