Indigenous-led Nature-based Solutions align net-zero emissions and biodiversity targets in Canada
Bibliographic record
Abstract
The compressed folders contain Supporting Information for the study "Indigenous-led Nature-based Solutions align net-zero emissions and biodiversity targets in Canada". The folder "R Notebooks + Final data only Zenodo.zip" contains an R Studio project file and R notebooks explaining in detail the processes of data analysis and final model statistics from the matching analyses and GAMMs. The R notebooks are organized as follows: R notebooks names General objective step_0 Analyze the descriptions of Indigenous-led NbS initiatives using topic modelling step1A - step1D Quantify the spatial-temporal patterns of carbon storage and a composite biodiversity index in Government funded and unfunded Indigenous Lands as well as in conventional Protected Areas using geospatial analyses. step2A - step3B Assess the effects of government funding of Indigenous-led NbS on carbon storage and biodiversity relative to Protected Areas using Matching Analysis and GAMMs step4A - step4B Prepare statiscal models data for plotting Please note that the datasets generated for this study were not released publicly to respect the privacy of Indigenous organizations and lands analyzed in our study. For inquiries about access to these datasets, please reach out to Graeme Reed (greed@afn.ca) for further information.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".