A Comparison of Variability in Two Cassin’s Auklet <i>(Ptychoramphus aleuticus)</i> Colonies Using Spline-based Nonparametric Models
Bibliographic record
Abstract
While variability in the reproductive performance of a population over time is a familiar and useful concept to ecologists, it can be difficult to capture mathematically. Commonly used ecological variability statistics, such as the standard deviation of the logarithm and coefficient of variation, discard the time-ordering of observations and consider only the unordered response variable values. We used a relatively new methodology, the cubic regression spline (a flexible curve fitted to a scatterplot of data), both to illustrate trends in reproductive performance over time and to explore the utility of the cubic regression spline roughness penalty (J) as a statistic for measuring variability while retaining time-ordering information. We concluded that although J measures variability in a mathematical sense, it can be inappropriate in a population ecology context because of sensitivity to small-scale fluctuations. To illustrate our methodology, we used the CRS approach in an analysis of historical data from two Cassin’s Auklet colonies located on Frederick and Triangle Islands in coastal BC, developing a model for the annual mean nestling growth rate on each island over seven contiguous years. Model selection indicated a complex (nonlinear) trend in growth rate on both islands. We report higher variability in the resident bird population of Triangle Island than Frederick Island, based on a comparison of the fitted curves, and the values of the coefficient of variation and population variability summary statistics.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".