Bibliographic record
Abstract
In Chapters 4 and 5, we examined different models of population growth where the structure of the population, in terms of age or size, was constant or unimportant and so could be ignored. However, we are well aware that such factors as sex and age have profound effects on the chances of an individual dying, or producing offspring, and so we need to incorporate some of these factors into our growth models. These vital statistics of populations are called demographics, and the study of these statistics is called demography. First, the pattern of mortality in relation to age is examined and quantified in Chapter 14. These age-specific death rates are combined with the age-specific birth rates in the following chapter to calculate the exponential growth rates of populations. Some populations with more complex growth characteristics cannot be modelled by the basic equations, and so matrix models of population growth are also introduced because they can be used to describe the growth of any population. Finally, Chapter 16 considers how the pattern of age-specific birth and death rates might have evolved by natural selection, followed by a brief review of the evolution of life-history traits of organisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".