Testing the Validity of Age-Size Reconstructions in Cohort Species, Using<i>Carnegiea gigantea</i>
Why this work is in the frame
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Bibliographic record
Abstract
Age-size relationships of a species in any given population are variable due to local environmental and genetic variations across individuals. The aim of this study is to test an age-size model for the keystone Carnegiea gigantea (saguaro, Cactaceae), that establishes in cohorts, to assess its accuracy in reconstructing those cohorts. Monte Carlo simulation is used to generate a Carnegiea gigantea population based on parameters selected and then applies the age-size model to the population to ascertain its effectiveness. Individuals in a cohort of different sizes are generated, as would be expected in the real world, and a simulated empirical dataset is created. Variation in growth over time incorporates two sources of variability, (1) individual variability (e.g. genetic or microsite variations) as well as (2) population-wide variability (such as fluctuations in rainfall from year to year). Generally, older cohorts are more difficult to accurately estimate, but all cohorts are identifiable. Results suggest that the Drezner model for Carnegiea gigantea is robust for reconstructing periods of establishment. This test of the Drezner model using annual and individual multipliers can be applied to other age-size models to ascertain their effectiveness, particularly for cohort identification.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it