A systematic approach for selecting focal species for conservation in the forests of Nova Scotia and Maine
Bibliographic record
Abstract
Focal species are a critical component of conservation planning, along with representation of ecosystems, special elements and ecologically sustainable management. They warrant conservation attention because they are functionally important, wide-ranging or space-demanding, habitat-quality indicators, ‘flagship’, and/or vulnerable or special populations. A delphi survey matrix-based approach, involving regional experts in the selection of potential focal species, was applied in Nova Scotia, Canada and Maine, USA. Matrices with native species on one axis and selection characteristics on the other axis were used to summarize expert knowledge and judgment. Characteristics were related to biological traits and habitat requirements that make some species more vulnerable than others in human-modified landscapes. In Nova Scotia and Maine, 19 and 11 experts, respectively, completed the matrices, which were subsequently numerically assessed. Species with the highest scores were identified as potential focal species, including wolf, cougar, lynx, river otter, eastern pipistrelle, wood turtle, four-toed salamander, golden eagle and Atlantic salmon. Concerns remain around the lack of representation of some classes of species, subjectivity in selecting and weighting characteristics, and the relative nature of assessing species against the characteristics. Accordingly, potential focal species should be subject to verification through more rigorous and quantitative analysis and monitoring. Nonetheless, if applied with care, the matrix-based approach can provide a relatively systematic and effective way of engaging regional experts in focal species selection.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".