Beyond description: the active and effective way to infer processes from spatial patterns
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
The ecological processes that create spatial patterns have been examined by direct measurement and through measurement of patterns resulting from experimental manipulations. But in many situations, creating experiments and direct measurement of spatial processes can be difficult or impossible. Here, we identify and define a rapidly emerging alternative approach, which we formalize as "space as a surrogate" for unmeasured processes, that is used to maximize inference about ecological processes through the analysis of spatial patterns or spatial residuals alone. This approach requires three elements to be successful: a priori hypotheses, ecological theory and/or knowledge, and precise spatial analysis. We offer new insights into a long-standing debate about process-pattern links in ecology and highlight six recent studies that have successfully examined spatial patterns to understand a diverse array of processes: competition in forest-stand dynamics, dispersal of freshwater fish, movement of American marten, invasion mechanisms of exotic trees, dynamics of natural disturbances, and tropical-plant diversity. Key benefits of using space as a surrogate can be found where experimental manipulation or direct measurements are difficult or expensive to obtain or not possible. We note that, even where experiments can be performed, this procedure may aid in measuring the in situ importance of the processes uncovered through experiments.
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 | 0.000 |
| 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) | 0.001 | 0.000 |
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