Bibliographic record
Abstract
What is landscape ecology? Although the definition of landscape ecology has been dealt with extensively (some would say ad nauseam) in the landscape ecological literature, there remains confusion among other ecologists as to exactly what landscape ecology is and, particularly, what its unique contribution is to ecology as a whole. Ecology is the study of the interrelationships between organisms and their environment (Ricklefs, 1979). The goal of ecological research is to understand how the environment, including biotic and abiotic patterns and processes, affects the abundance and distribution of organisms (Fig. 1.1). This includes indirect effects such as the effect of an abiotic process (e.g., fire) on a biotic process (e.g., germination), which in turn affects the abundance and/or distribution of an organism. Processes considered are typically at a “local” scale, that is, at the same scale or smaller than the scale of the abundance/distribution pattern of interest. Landscape ecology, a subdiscipline of ecology, is the study of how landscape structure affects the abundance and distribution of organisms (Fig. 1.2). Landscape ecology has also been defined as the study of the effect of pattern on process (Turner, 1989), where “pattern” refers specifically to landscape structure. The full definition of landscape ecology is, then, the study of how landscape structure affects (the processes that determine) the abundance and distribution of organisms. In statistical parlance, the “response” variables in landscape ecology are abundance/distribution/process variables, and the “predictors” are variables that describe landscape structure.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".