Bibliographic record
Abstract
Many of the leading ecological and evolutionary characteristics of populations are governed by their effective population size, which in turn is strongly influenced by the minimum census size. The succession of minima of increasing rank R in time is described by the expected value of the next minimum ω R and by the expected time T R elapsing before it occurs. The relationships of ω R and T R with R together determine the minimal population expected to be encountered within a given period of time. These relationships depend on the dynamic model for species abundance. The four main types of model investigated here have characteristically different successions. – Random: ω R log‐linear and T R log‐linear on R; – Forced: ω R linear and T R log‐linear on R; – Neutral community: ω R linear and T R linear on R; – Foodweb: ω R log‐linear and T R linear on R; Data on species abundance in long‐term surveys of plankton communities suggests that the Foodweb model best represents nature. This implies that each species is likely to encounter new minima on time‐scales much shorter than species longevity. Either local selection and dispersal or large population size may help to alleviate frequent bottlenecks, but it is likely that the internal dynamics of trophically structured communities may lead to continual change in species composition.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".