Latent health factor index: a statistical modeling approach for ecological health assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multimetric indices (MMIs) are appealing scalar-valued tools for policy makers when rating ecosystems with respect to biological conditions that are not directly measurable. For conventional assessment of ecological health using MMIs, the quantitative calibration of health qualities can be specific to the investigator, and to the geographical region and time frame of interest. We propose a statistical-model-based approach that provides a systematic mechanism to construct MMIs; our approach aims to address some common issues of conventional practices, including the loss of information from data, spatio-temporal restrictions, and concerns over arbitrariness and costs. Our latent health factor index (LHFI) is obtained via statistical inference for an unobservable health factor term in a mixed-effects analysis-of-covariance regression that directly models the relationship among metrics, a very general notion of health, and factors that can influence health. We illustrate the approach by constructing an LHFI for a freshwater system using benthic taxonomic data in various Bayesian hierarchical formulations of generalized linear mixed models, implemented by Markov chain Monte Carlo techniques. The concept of the LHFI is also applicable to medical and other contexts. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it