Bibliographic record
Abstract
In a broad sense, landscape ecology is the study of environmental relationships in and of landscapes. But what are “landscapes”? Are they heterogeneous mosaics of interacting ecosystems? Particular configurations of topography, vegetation, land use, and human settlement patterns? A level of organization that encompasses populations, communities, and ecosystems? Holistic systems that integrate human activities with land areas? Sceneries that have aesthetic values determined by culture? Arrays of pixels in a satellite image? Depending on one's perspective, landscapes are any or all of these, and more. Landscape ecology is therefore a diverse and multifaceted discipline, one which is at the same time integrative and splintered. The promise of landscape ecology lies in its integrative powers. There are few disciplines that cast such a broad net, that welcome – indeed, demand – insights from perspectives as varied as theoretical ecology, human geography, land-use planning, animal behavior, sociology, resource management, photogrammetry and remote sensing, agricultural policy, restoration ecology, or environmental ethics. Yet this diversity carries with it traditional ways of doing things and different perceptions of the linkages between humans and nature, and these act to impede the cohesion that is necessary to give landscape ecology conceptual and philosophical unity. The contributions we have collected here do not produce that cohesion, but they demonstrate with remarkable clarity the elements from which we must forge this unification. Individually and collectively, they provide glimpses into the varied ways that landscape ecologists think about landscapes and about what landscape ecology is (or isn't).
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.337 | 0.167 |
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".