National Restoration Analysis Results
Bibliographic record
Abstract
The file "NationalRestorationAnalysisHexbins" represents the output data file from WWF-Canada's assessment of the relative priority of restoring converted landscapes based on potential to store carbon over the long term, as well as enhance biodiversity through habitat provisioning. <br> The results are represented at the 100km^2 hexbins with the following fields. T_Carbon_P = Estimated net potential of converted lands in the hexbin to store carbon following complete restoration B_Carbon_P = Estimated net potential of converted lands in the hexbin to store carbon in biomass following complete restoration S_Carbon_P = Estimated net potential of converted lands in the hexbin to store carbon in soil following complete restoration E_STARr_P = Cumulative STARr metric calculated for all endangered taxa in the hexbin* T_STARr_P = Cumulative STARr metric calcualted for all taxa in the hexbin* LC_Wetland = Estimated area of converted lands in the hexbin which would natural support wetland habitat in km^2 LC_Forest = Estimated area of converted lands in the hexbin which would natural support forest habitat in km^2 LC_Grassla = Estimated area of converted lands in the hexbin which would natural support grassland habitat in km^2 LC_Shrubla = Estimated area of converted lands in the hexbin which would natural support shrubland habitat in km^2 LC_Lichen = Estimated area of converted lands in the hexbin which would natural support exposed lichen habitat in km^2 LC_Barren = Estimated area of converted lands in the hexbin which would natural support exposed barren habitat in km^2 Hexbin_are = Total area of hexbin in km^2 Human_area = Total area of hexbin represented by converted lands in km^2 Human_Prop = Portion of hexbin area represented by converted lands ROOT_5Mha = Output selection frequency of ROOT optimizer for an area-based restoration target of 5 million ha (50,000 km^2) , higher numbers indicating greater agreement ROOT_10Mha = Output selection frequency of ROOT optimizer for an area-based restoration target of 10 million ha (100,000 km^2) , higher numbers indicating greater agreement ROOT_15Mha = Output selection frequency of ROOT optimizer for an area-based restoration target of 15 million ha (150,000 km^2) , higher numbers indicating greater agreement <br> *for description of STARr metric see: Mair, L., Bennun, L.A., Brooks, T.M. et al. A metric for spatially explicit contributions to science-based species targets. Nat Ecol Evol 5, 836–844 (2021). https://doi.org/10.1038/s41559-021-01432-0
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.042 | 0.023 |
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; both teacher heads agree on what is shown here.
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".