Farm management fragmentation in Nova Scotia does not affect farm habitat provision
Bibliographic record
Abstract
Abstract Farmland comprises an important opportunity for biodiversity conservation worldwide. The likelihood of a farmer fostering habitat for biodiversity has been studied from the perspective of personal or economic drivers. Beyond farm area, the geographic characteristics of the farmland itself—such as parcel sizes, numbers, and distribution—are rarely considered. This paper uses a landholder survey (n = 350, 37% response rate) to explore the variety of farmland management fragmentation and its implications for habitat on farms, specifically ponds, wetlands, and woodlands. This exploratory research was implemented in Nova Scotia, a relatively long‐settled province of Canada, which is subject to inheritance, farmland abandonment, and farm expansion. An index and a typology of management fragmentation were developed, based on geographical impacts (increased distance to travel and edge per unit area), drawing on hypotheses about how geography may affect farmer decision making about habitat. Results suggest that farm size matters more than fragmentation, but also that perceptions of ecosystem services from each ecosystem may have an impact. Suggestions for further research are provided, including alternative methods that could be used and testing these insights in more homogenous agricultural landscapes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".