Estimating population variability of aphids (Hemiptera: Aphididae): how many years are required?
Bibliographic record
Abstract
Abstract Variability is an important characteristic of population dynamics, but the length of the time series required to estimate population variability is poorly understood. To this end, population variability of Macrosiphum euphorbiae (Thomas), Myzus persicae (Sulzer), and Aphis nasturtii (Kaltenbach) (Hemiptera: Aphididae) was investigated. Population variability (measured as PV, a proportion between 0 and 1) was estimated for time series of 3–62 years, giving replicate estimates for time series of 3–20 years that were normally distributed. Mean values for PV were more uniform for a time series of 12 years or longer than for shorter ones. The standard deviation of PV declined to a minimum at 12–15 years, as the length of the time series increased. Discrimination of estimates of PV was reliable for 15-year time series and longer, but not necessarily for shorter ones. Although M. euphorbiae had a relatively low PV, the coefficient of variation of that PV (12.5), was higher than for the other two species (3.5, 4.5). For robust estimates of PV, a time series of 15 years is recommended, because it minimises the standard deviation of PV, and discriminates values of PV that differ by 0.06 on a 0–1 scale.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".