Ion Cycling in Hemlock–Northern Hardwood Forests of the Southern Lake Superior Region
Bibliographic record
Abstract
Upland forests of the southern Lake Superior region are diverse and contain a shifting mosaic of eastern hemlock [Tsuga canadensis (L.) Carr.] and northern hardwood forests dominated by sugar maple (Acer saccharum Marsh.). In this study, we survey the relative effects of management practice (old growth vs. managed), forest cover type (hemlock vs. northern hardwood), and soil great group (Entic Haplorthod vs. Alfic Oxyaquic Fragiorthod) on ion cycling as a precursor to a longer-term, more detailed study. Bulk precipitation, throughfall, and soil leachates at three depths were collected for two growing seasons in eight stands on the Ottawa National Forest in the Upper Peninsula of Michigan. A total of 1210 solutions were analyzed for pH, Na, K, Mg, Ca, Cl, NO3, and SO4. Losses of base cations (Ca, Mg, K) and SO4 from the bottom of the rooting zone generally were greater in old-growth than in managed northern hardwoods on both fragic and nonfragic soils. Leaching losses of base cations and NO3 usually were greater beneath old-growth northern hardwoods than beneath old-growth hemlock on both soil types and for both forest cover types and management practices on fragic than nonfragic soils. Management practice, forest cover type, and soil type all appear to affect ion cycling within these forests. All of the stands featured striking losses of base cations that probably are influenced strongly by NO3 and SO4 in atmospheric deposition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".