Earthworm and Soil Data for Ottawa National Forest
Bibliographic record
Abstract
Two types of analysis were run. I. Probability of finding L. terrestris in habitat types of the Ottawa National Forests. Abstract: Parts of the Ottawa National Forest (ONF) provide suitable habitat for invasive earthworms. Extensive earthworm invasion is a relatively recent event on the ONF and this study captures the current state of the earthworm invasion through a four-stage invasive species distribution model (iSDM). The random distribution of earthworms indicates early colonization by earthworms which is moderated by habitat (forest type, soil group, and drainage class). CART modeling was used to determine probability of earthworm invasion. The CART model had a relatively low R2 (0.256) which is a result of the early stage of the earthworm invasion. The probability of finding L. terrestris increased in habitat which is generally considered better habitat. II. Changes in Carbon and Organic Matter due to earthworm invasion on the Ottawa National Forest. Abstract: Earthworms, due to their feeding habits, may impact O horizons and soil C, an important component in the global carbon cycle. We compared O horizon thickness (O, Oi, Oe, Oa) and %C in the O, Oi, Oe, and Oa horizons; in mineral soil layers with thicknesses of 0-15 (defined as M1), 15-30 (defined as M), and 0-30 (defined as M) cm; and a ratio of %C in M1:M2 using t-tests. Separate t-tests on each of the soil horizons/layers/ratios were conducted with two dependent variables: the presence/absence of L. terrestris and the presence/absence of all earthworm species. When necessary a general linear model was developed to further explore the C relationships at site with different landscape characteristics. Similar to many other studies we found no relationship between %C and the class variables. Organic horizons had horizon depths that were significantly smaller at sample sites where earthworms were present.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".