Bibliographic record
Abstract
Introduction We would like to be able to predict the dynamics of introduced plant species in different situations, how they might respond to biological control, and how they might spread. Several different types of models have been used to integrate information on the populations of introduced species and their control. These models include (1) simulation models based on individual population units that can vary depending on survival and reproduction functions estimated from field studies and may involve stochasticity, (2) analytical models in which functions derived from simulation models or field measurements are used to describe the population processes, and (3) matrix models based on life table studies. In Chapter 5 we described the most basic aspects of population ecology – birth, immigration, death and emigration – and discuss how life tables could be used to summarize data on the transitions among different life stages. Also we described how the rate of growth, R 0 or λ, of a population could be determined by relating the population density of one generation to that of the next. In this chapter we explore theoretical models of biological control, the use of models to study populations of introduced plant species, and then models of the spread of introduced species. The strengths and weaknesses of different models will be evaluated. A more extensive treatment of models of weed populations can be found in Cousens and Mortimer (1995).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".