A Simple Model to Predict the Temporal Nitrogen Saturation Point of a Jack Pine (Pinus banksiana L.) Forest
Bibliographic record
Abstract
Dry jack pine forests exposed to elevated nitrogen (N) deposition do not necessarily exhibit traditional N saturation responses. Using empirical results from a five year above-canopy N deposition experiment, a simple nitrogen (N) saturation model was developed for jack pine (Pinus banksiana Lamb.) forests dominated by cryptogams. For the model, a series of differential equations using empirically derived rate constants (k) were applied to estimate changes in net N pools in biotic and abiotic components across a narrow N deposition gradient (0, 5, 10, 15, 20, and 25 kg N ha−1 yr−1). Critical soil C:N ratios were used as the model limit to signify saturation. We explored the saturation response time by priming the model to mineralize approximately one percent of the soil N pool after the critical C:N ratio was reached. A portion of this pool was made available to jack pine trees. Nitrogen leaching below the rooting zone occurred when the mass of N mineralized from the soil organic- and A horizon layers exceeded the theoretical mass required by jack pine, driving the mineral soil below the critical C:N ratio. The model suggests that N leaching below the rooting zone could happen around 50 (1% LFH N mineralization) years after the onset of deposition at 25 kg N ha−1 yr−1. In contrast, N deposition rates ≤ 20 kg N ha−1 yr−1 are not expected to be associated with N leaching over this timeframe. The modeled results are consistent with empirical surveys of jack pine forests exposed to elevated N deposition for several decades.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".