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
I investigate the efficacy of the Moran effect as applied to natural population processes. The Moran effect, the correlated density‐independent disturbances that bring independently oscillating local populations into synchrony, was originally conceived as an attribute of a linear model system. However, it applies only approximately to natural populations, as they are inherently nonlinear in their density‐dependent structure, given that no animal has an unlimited reproductive capacity. The degree of approximation, as measured by the degree of correlation among populations involved, is shown to depend, given the density‐dependent structure, on the variances of the random disturbances. In particular, if the unperturbed density‐dependent process converges to an equilibrium density, approximation is good when the variances are equal among the populations involved and comparatively small, but it worsens as the variances and their differences increase. For those processes that do not converge, when unperturbed, but exhibit bounded oscillations, the degree of approximation tends to deteriorate considerably, or may practically collapse, even if the disturbances are not large in variance. A sample correlation coefficient is often spurious if the observed population processes to be correlated are highly autocorrelated and limited in length. To detect spuriousness, the density‐independent disturbances must somehow be estimated from the data. Three methods (moving‐average, linear regression, and nonlinear regression) are considered, and their merits and demerits are discussed. Results of the present investigation are summarized with respect to the interpretations (or diagnoses) of sample cross‐correlation functions.
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) | 0.002 | 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 it