Reflections on twenty-five years of landscape ecology discussion in the German-speaking IALE community
Bibliographic record
Abstract
In May 1999, about 30 people from Austria, Germany and Switzerland met in Basel (Switzerland) to found the German Chapter of the International Association for Landscape Ecology (IALE-D) to bring together Germanspeaking researchers, planners and other people interested in landscape ecology. Now, twenty-five years later, we take this milestone as an opportunity to reflect on the evolution of the topics that have shaped the landscape ecology discourse within the IALE-D community. In this editorial, we (1) present the history of the IALE-D conferences, (2) reflect on the topics addressed by the conference contributions and how they have developed, and (3) offer some initial indications of changes in relevance with regard to technological advances, thematic foci, transdisciplinarity, sustainability issues, and cultural dimensions that can be observed over time. Furthermore, we provide an overview of the articles published in 2024 in Landscape Online, which mainly reflect two emerging topics, which follow the lines of the thematic development of the IALE-D conferences, that is, a focus on urban environments as well as on pressing issues related to global change.
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 | 0.035 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".