NOVEL MECHANISMS FOR NITROGEN STORAGE, TRANSPORT, AND UPTAKE
Bibliographic record
Abstract
Nitrogen (N) plays a critical and complex role in the Earth's ecosystems and is often a limiting nutrient in agriculture. The work presented here investigates four aspects of the N cycle. Chapter 1 examines interactions between pyrogenic organic matter (PyOM) and ammonia (NH3). Adsorption isotherms, spectroscopy, and stoichiometric analyses show that PyOM’s NH3 retention capacity can exceed 180 mg N g-1 PyOM carbon. More than half of the NH3–N is retained through chemisorption, including the formation of a variety of covalent bonds. These results indicate that PyOM could exert an important and unaccounted-for control on global N cycling. Chapter 2 explores biochar’s capacity to improve N retention during composting. When N loss was calculated as a proportion of C loss to account for differences in microbial activity, relative N loss from compost with oxidized biochar was more than fivefold lower than N loss from compost with unoxidized biochar and comparable to relative N loss from the compost feedstocks alone. N retention by oxidized biochar was directly responsible for lower N loss from compost. These data show that biochar can be used to improve compost efficiency and that biochar’s physiochemical characteristics influence its performance in compost. Chapter 3 investigates multipartite plant-biotic synergies that increase plant N acquisition more than tenfold and account for half of the N that mycorrhizal plants acquire from soil organic matter. This relationship may contribute to more than 70 Tg of annually assimilated plant N, thereby playing a critical role in global nutrient cycling and ecosystem function. Chapter 4 provides evidence of subsurface plant acquisition of N from NH3 gas. Plants derived up to 34% of total daily N from NH3. Nearly 4% of N in soil organic matter traveled as NH3 gas belowground and accounted for over 9% of N acquired by plants per season. Together, the results presented here could be used to better understand the global N cycle and improve sustainable N delivery to crops.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".