A mixture model-based approach to the classification of ecological habitats using Forest Inventory and Analysis data
Bibliographic record
Abstract
A Gaussian mixture model (GMM) is used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the northeastern USA. The GMM approach captures intra-class variation by modeling each habitat class as a mixture of subclasses of Gaussian distributions. The classification is achieved based on the appropriate posterior probability. The GMM classifier outperforms a traditional statistical method (i.e., linear discriminant analysis or LDA), and produces similar overall accuracy rates to a commonly used neural network model (i.e., multi-layer perceptrons or MLP). For the classifications of individual ecological habitats, however, MLP produces better (or same) producers' classification accuracies for five of the six ecological habitats than does GMM. But the GMM's accuracy rates are more consistent (92%97%) across the six ecological habitats than those of the MLP model (82%99%). This study shows that GMM offers an attractive alternative for modeling the complex stand structure and relationships between variables in mixed-species forest stands.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".