Connectivity for conservation: a framework to classify network measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graph theory, network theory, and circuit theory are increasingly being used to quantify multiple aspects of habitat connectivity and protected areas. There has been an explosive proliferation of network (connectivity) measures, resulting in over 60 measures for ecologists to now choose from. Conceptual clarification on the ecological meaning of these network measures and their interrelationships is overdue. We present a framework that categorizes network measures based on the connectivity property that they quantify (i.e., route-specific flux, route redundancy, route vulnerability, and connected habitat area) and the structural level of the habitat network to which they apply. The framework reveals a lack of network measures in the categories of "route-specific flux among neighboring habitat patches" and "route redundancy at the level of network components." We propose that network motif and path redundancy measures can be developed to fill the gaps in these categories. The value of this framework lies in its ability to inform the selection and application of network measures. Ultimately, it will allow a better comparison among graph, network, and circuit analyses, which will improve the design and management of connected landscapes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it