Bibliographic record
Abstract
This dataset contains scripts and derived outputs used to identify and quantify intact forests and natural ecosystems in Nova Scotia, Canada. The workflows implement a regional adaptation of the intact forest landscape concept (Potapov et al. 2008) using fine-scale provincial forest inventory data and multiple disturbance datasets. The scripts automate geospatial processing steps, including forest selection, disturbance removal, minimum bounding geometry analysis, area, width, and length threshold filtering, shape complexity calculations, and spatial overlap analyses with protected areas. Analyses were conducted across multiple area thresholds to reflect varying ecological scales relevant to wildlife movement and landscape connectivity. All spatial analyses were performed in ArcGIS Pro using ArcPy and projected to EPSG 22820. The dataset supports full reproducibility of the results presented in the associated manuscript. This dataset is part of a manuscript currently under review entitled “Adapting the intact forest landscape concept to regional contexts in Nova Scotia, Canada”. Reference Potapov, P., Yaroshenko, A., Turubanova, S., Dubinin, M., Laestadius, L., Thies, C., Aksenov, D., et al. 2008. Mapping the world’s intact forest landscapes by remote sensing. Ecology and Society 13(2):51.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".