Biogeochemical responses to multiyear above-canopy applications of nitrogen at a jack pine (<i>Pinus banksiana</i>) forest in northern Alberta, Canada
Bibliographic record
Abstract
Elevated atmospheric nitrogen (N) deposition can alter forest biogeochemistry leading to adverse impacts on terrestrial ecosystems. Experimental studies often apply N to the forest understorey at greatly elevated loads, bypassing important canopy interactions. Using a narrow N deposition gradient (0, 5, 10, 15, 20, and 25 kg N·ha−1·year−1), we measured changes in jack pine ( Pinus banksiana Lamb.) forest biogeochemistry in the bituminous sands region of northern Alberta, Canada after 5 years of above-canopy N additions. The canopy intercepted approximately 46% of applied N across all treatments, but during the final year, N interception was reduced to 7% in the highest treatment. Nitrogen concentrations in nonvascular organisms increased with treatment and N was also immobilized in decomposing litter in the highest treatment. Otherwise, vascular plant biomass, foliar chemistry, and soil processes exhibited no relationship with treatment over 5 years. This work suggests that jack pine forests in the region have a high capacity to immobilize N inputs over the short-term (5 years), which restricts other biogeochemical responses traditionally associated with elevated N deposition.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".