Appendix A. Maps of Nova Scotia showing distribution of (Fig. A1) moose populations and protected areas; (Fig. A2) moose habitat suitability values; (Fig. A3) road density and moose pellet presence/absence; (Fig. A4) contiguous areas of natural cover 10,000 ha; (Fig. A5) roadless areas; (Fig. A6) uneven-aged forest stands; (Fig. A7) combined cover for contiguous natural cover 10,000 ha, roadless areas, and uneven-aged forest stands; (Fig. A8) areas of primary priority combining natural...
Bibliographic record
Abstract
Maps of Nova Scotia showing distribution of (Fig. A1) moose populations and protected areas; (Fig. A2) moose habitat suitability values; (Fig. A3) road density and moose pellet presence/absence; (Fig. A4) contiguous areas of natural cover 10,000 ha; (Fig. A5) roadless areas; (Fig. A6) uneven-aged forest stands; (Fig. A7) combined cover for contiguous natural cover 10,000 ha, roadless areas, and uneven-aged forest stands; (Fig. A8) areas of primary priority combining natural areas, 10,000 ha, uneven-aged forest stands, and roadless areas; (Fig. A9) species at risk globally or provincially; (Fig. A10) highest rarity-weighted richness values; (Fig. A11) significant ecosites; (Fig. A12) signigficant old and unique forest stands; (Fig. A13) areas of primary priority for special elements; highest habitat suitability and population densities for (Fig. A14) American moose, (Fig. A15) American marten, and (Fig. A16) Northern Goshawk; (Fig. A17) 47 core areas selected by priority sites for representation, special elements, and focal species; (Fig. A18) cost-surface for American marten; and (Fig. A19) least-cost paths for American marten.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.384 | 0.093 |
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".