Model complexity and information in the data: Could it be a house built on sand?
Bibliographic record
Abstract
Heisey et al. (2010), in an interesting paper, try to address a very difficult problem of analyzing spatially referenced, age specific prevalence data.The general goal of the analysis is to understand how force of infection changes as a function of age, time, and space.To further complicate matters, all the data considered in the paper are censored observations.Binary data are notoriously difficult to analyze, especially when latent processes are involved and prevalence is very low.Frankly, I was surprised by the complexity of the models they consider and the limited amount of information available to fit these models.I would like to congratulate them for trying to address such a difficult problem and in the process bringing to the attention of the ecologists some important statistical models in survival analysis.How does one generally deal with the conflicting issues of lack of information and desire to conduct inference about complex underlying processes?The standard approach is to compensate for lack of information by adding assumptions.This is done routinely in most statistical analyses by assuming a parametric model.For example, one can conduct inference in ANOVA without assuming any specific relationship between the treatment means if replicate observations are available at each treatment level.If such replicate data are not available, instead of giving up, we assume that there is a linear (or, some parametric) relationship between the covariates and the response, the regression approach.This is a smoothing assumption.Similarly, in one of the fundamental papers on statistical inference in the presence of nuisance parameters, Kiefer and Wolfowitz (1956) showed that simply assuming that the nuisance parameters arise from a distribution is enough of a smoothing assumption to estimate not only the parameters of interest but also the distribution function from which nuisance parameters are assumed to have arisen.Heisey et al. (2010) try to get away with the limited information available in the prevalence data, where all observations Ecology, Vol.91, No.
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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 | 0.013 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.031 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".