Synthesis: Ecological Connectivity in the Context of Socio-Ecological Production Landscapes and Seascapes (SEPLS)
Bibliographic record
Abstract
This chapter provides the synthesis of the findings from the 12 case studies presented in this volume. It addresses the following questions: (1) how ecological connectivity is conceptualized in the context of managing socio-ecological production landscapes and seascapes (SEPLS); (2) how to measure and evaluate ecological connectivity and monitor its level and progress for benefiting people and nature through managing SEPLS; and (3) how challenges are addressed and opportunities are seized to ensure and enhance ecological connectivity through managing SEPLS for biodiversity, ecosystems, and human well-being. Building on the synthesis of the case study findings, this chapter also offers policy recommendations that could support favoring ecological connectivity to ensure ecologically and socially sound outcomes. These recommendations illustrate ways to help simultaneously achieve multiple global goals and targets for sustainability and biodiversity, including the sustainable development goals (SDGs) (e.g., SDGs 1, 2, 3, 11, 13, 14, 15, and 17) and the goals and targets of the Kunming-Montreal Global Biodiversity Framework (e.g., Goal A, Targets 1, 2, 3, and 11).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".