Testing the Validity of Age-Size Reconstructions in Cohort Species, Using<i>Carnegiea gigantea</i>
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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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".