Growing the biphasic framework: Techniques and recommendations for fitting emerging growth models
Bibliographic record
Abstract
Abstract Several new growth models have been proposed to account for the life‐history trade‐offs that occur when indeterminately growing species allocate energy between somatic growth and reproduction. These models can improve the understanding of lifetime growth and life history, but can be more difficult to fit than conventional growth models. Increased data demands, multiple growth phases and increased parameterization all serve as barriers to the adoption and proper use of these new models. We review and comment on confounding issues during model fitting for several of these models, and provide advice on surmounting such issues. We then simulation‐test an example model, the Lester biphasic growth model, using several common fitting approaches. We highlight the biases and precision of each approach and provide guiding documents using r and jags code. The Bayesian Markov chain Monte Carlo and likelihood profiling approaches generally provided the best fits. Simpler approaches can be unbiased and precise if sampled data are of relatively high quality (e.g. moderate sample sizes for juvenile and adult phases) and model assumptions are met. Bayesian hierarchical approaches can accommodate more complicated data scenarios (e.g. unbalanced design across multiple populations); we provide an example of such an approach by recovering growth trajectories and inferring growth‐associated trait variation and environmental effects across multiple populations. Conventional growth models provide limited inference on life history. Many biphasic growth models can provide direct inference on multiple life‐history traits, but can be difficult to fit. The recommended approaches herein provide a path forward for fitting biphasic growth models in a variety of scenarios, allowing for wider application and tests of life history and ecological theory.
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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 | 0.046 | 0.192 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".