Multidecadal trajectories of soil chemistry and nutrient availability following cutting vs. burning disturbances in Upper Great Lakes forests
Bibliographic record
Abstract
What are the successional trajectories and impacts of disturbances on forest soil nutrient availability? Answers remain elusive because the time scale of interest is long and many factors affect soil properties. We address this question on a regionally representative landscape in northern Michigan, U.S.A. Late-successional reference stands aside, most forests on this landscape were clearcut and burned between 1870 and 1911; subsequently, stands comprising two chronosequences were either cut and burned again, or cut only, at multidecadal intervals. Influences of disturbance and succession were detectable in A, B, and C horizons, particularly for properties affected by ash deposition: pH, Ca, and Mg declined with age but were higher in twice-burned stands. A horizon NH4+ was lower in twice-burned than once-burned stands and declined with age in both chronosequences. B horizon Fe increased with age in both chronosequences but remained lower in twice-burned stands, suggesting slower recovery of pedogenesis following more severe disturbance. Contrasted against A and B horizons, where soil properties were driven by disturbance and succession, textural influences were evident in C horizons through variation in Ca, Mg, K, Al, and cation exchange capacity. Collectively, these results indicate deep, long-lasting disturbance impacts and a bottom-up influence of parent material at the landscape level.
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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 | 0.000 | 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) | 0.001 | 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".