Bibliographic record
Abstract
In the last decade, we have seen the emergence and consolidation of a conceptual framework that recognizes the landscape as an ecological unit of interest. Plant ecologists have long emphasized landscape‐scale issues, but there has been no recent attempt to define how landscape concepts are now integrated in vegetation studies. To help define common research paradigms in both landscape and plant ecology, we discuss issues related to three main landscape concepts in vegetation researches, reviewing theoretical influences and emphasizing recent developments. We first focus on environmental relationships, documenting how vegetation patterns emerge from the influence of local abiotic conditions. The landscape is the physical environment. Disturbances are then considered, with a particular attention to human‐driven processes that often overrule natural dynamics. The landscape is a dynamic space. As environmental and historical processes generate heterogeneous patterns, we finally move on to stress current evidence relating spatial structure and vegetation dynamics. This relates to the concept of a landscape as a patch‐corridor‐matrix mosaic. Future challenges involve: 1) the capacity to evaluate the relative importance of multiple controlling processes at broad spatial scale; 2) better assessment of the real importance of the spatial configuration of landscape elements for plant species and finally; 3) the integration of natural and cultural processes and the recognition of their interdependence in relation to vegetation management issues in human 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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".