Integrating carbon stocks and landscape connectivity for nature-based climate solutions
Bibliographic record
Abstract
This project aimed to integrate maps of terrestrial carbon stocks (Sothe et al. 2022) and landscape connectivity (Pither et al. 2021) in Ontario, Canada at 300m spatial resolution to identify areas important for nature-based climate solutions. This dataset contains 2 maps — one examining the overlap of current density estimates from circuit theory (proportional to the probability of animal movement), with forest carbon storage estimates (above-ground biomass, below-ground biomass, and dead plants) and the other current density with soil carbon storage estimates (0 - 1m depth). We used a bivariate mapping approach to identify important areas of overlap between current density and carbon layers. We calculated quantiles at 20% intervals for the current density, forest, and soil carbon layers and then reclassified cell values for each raster layer with a value of 1 - 5 based on which percentile range they fell within. We then overlapped the current density map with each carbon map to produce two bivariate maps for current density by forest carbon and current density by soil carbon. Cell values for the bivariate maps were calculated using all unique combinations of the two layers, which resulted in 25 different cell values (5 × 5 matrix). The data are rasters with values ranging from 1 - 25 representing a varying degree of importance for each layer. Please refer to the publication for detailed methodology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".