A quantitative approach to defining soil nutrient regimes within ecosystem classifications for Northwestern Ontario
Bibliographic record
Abstract
Soil nutrient regimes (SNRs) are often incorporated in ecosystem classifications. Evaluation of actual nutrient levels associated with these SNRs and the development of complementary soil chemistry regimes (SCRs) could broaden their utility. Using data from 618 forest stands in northwestern Ontario, we developed five-category SCRs using K-means clustering and examined relationships among individual nutrients, SCRs, and the SNRs of the Canadian National Vegetation Classification Associations and the Ontario Ecological Land Classification Ecosites. F, A, and B horizon samples were analyzed for organic C (OrgC), total N (TotN), C:N ratio (C:N), cation exchange capacity (CEC), exchangeable bases, base saturation (BaSat), and pH. CEC, pH, and BaSat showed good correspondence across horizons, and together with C:N accounted for much of the variation in chemical properties. There was broad agreement between Association and Ecosite SNRs and B horizon (BHorz) and All horizon (AllHorz) SCRs. C:N decreased while pH and cation metrics increased with increasing SNR and SCR richness. User's accuracies (SNRs vs. SCRs) for the classifications ranged from 31%–39% but increased to 80%–86% for SNR values within ±1 SCR class. Classification trees identified pH class, soil texture, and overstory composition as the principal field-measured factors related to BHorz SCRs.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".