Precipitation control over inorganic nitrogen import–export budgets across watersheds: a synthesis of long‐term ecological research
Bibliographic record
Abstract
Abstract We investigated long‐term and seasonal patterns of N imports and exports, as well as patterns following climate perturbations, across biomes using data from 15 watersheds from nine Long‐Term Ecological Research (LTER) sites in North America. Mean dissolved inorganic nitrogen (DIN) import–export budgets (N import via precipitation–N export via stream flow) for common years across all watersheds was highly variable, ranging from a net loss of − 0·17 ± 0·09 kg N ha−1mo−1 to net retention of 0·68 ± 0·08 kg N ha−1mo−1. The net retention of DIN decreased (smaller import–export budget) with increasing precipitation, as well as with increasing variation in precipitation during the winter, spring, and fall. Averaged across all seasons, net DIN retention decreased as the coefficient of variation (CV) in precipitation increased across all sites (r2 = 0·48, p = 0·005). This trend was made stronger when the disturbed watersheds were withheld from the analysis (r2 = 0·80, p < 0·001, n = 11). Thus, DIN exports were either similar to or exceeded imports in the tropical, boreal, and wet coniferous watersheds, whereas imports exceeded exports in temperate deciduous watersheds. In general, forest harvesting, hurricanes, or floods corresponded with periods of increased DIN exports relative to imports. Periods when water throughput within a watershed was likely to be lower (i.e. low snow pack or El Niño years) corresponded with decreased DIN exports relative to imports. These data provide a basis for ranking diverse sites in terms of their ability to retain DIN in the context of changing precipitation regimes likely to occur in the future. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".