Fitness Estimation for Ecological Studies: An Evaluation in Columbian Ground Squirrels
Bibliographic record
Abstract
The topics of evolution by natural selection and ecological interactions are closely intertwined. Thus, measurements of evolutionary fitness are ubiquitous in the ecological literature. As an empirical problem, the components of fitness, reproduction and survival, may be analyzed to produce fitness estimates in several ways. These can be divided into annual estimates that are most appropriate for short-term (e.g., annual) and experimental studies, and lifetime fitness estimates that are most appropriate for evaluating functional organismal traits. The latter are appropriate for comparative studies¬¬ of natural selection in species with lifetimes that extend over several years. These estimates may also be particularly useful for estimating the direct and indirect components of inclusive fitness, an important topic for the evolution of cooperation. We present examples of some of these alternatives from our research on Columbian ground squirrels (Urocitellus columbianus). Using empirical data, we also test the degree of correspondence of annual fitness, lifetime reproductive success, and individual fitness measures that are based on matrix methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".