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
Abstract The first step in understanding ecological processes is to identify their spatial patterns. Ecological data are usually characterized by spatial structures and as such, they are said to be spatially autocorrelated. Spatial autocorrelation refers to the pattern where the values of a quantitative variable are more similar at nearby locations than expected by chance alone. Most ecological data exhibit some degree of spatial autocorrelation that is modulated by the spatial sampling design used to record the data and the method used to analyze them. Furthermore, ecological data can be the end result of several processes operating at different spatial scales. In such cases, ecological data are a composite of large‐scale trends at the macroscale, gradients and patchiness at mesoscale, and random patterns at local and microscales. Hence, to estimate the magnitude and the extent of spatial autocorrelation, various spatial statistics can be used. Here, we review the spatial statistics most commonly used by ecologists.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.068 | 0.002 |
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