USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST
Bibliographic record
Abstract
Our study applies the machine learning method, Random Forest (RF), to understand distribution patterns and predictive powers of environmental variables determining earthworm occurrence in northern hardwood forests of the Great Lakes region. In our study we found earthworm species: Dendrobaena octaedra (Savigny), Lumbricus rubellus (Hoffmeister), Lumbricus terrestris (L.), Aporrectodea rosea (Saigny), Aporrectodea calignosa (Saigny), and Aporrectodea tuberculata (Eisen). Presence/absence data of L. terrestris were used in predictive distribution modeling for the Ottawa National Forest in the Upper Peninsula of Michigan based on the following Geographic Information Systems (GIS) variables: forest cover type, soil texture, soil pH, and distance from roads. Random Forest results were successful in producing models with high predictive accuracies and stable environmental variables when predicting L. terrestris occurrence. Deciduous cover type contributed the most to the outcome of the RF models, followed by soil texture, distance from roads and soil pH. The effectiveness of this approach in modeling earthworm distribution could be the first step in leading a large-scale predictive modeling effort to determine earthworm distribution for all of the Great Lakes region and other northern hardwood forest ecosystems. Having this insight would advance forest management efforts and regional studies addressing earthworm ecological effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".