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Record W6977184360 · doi:10.7298/1qj7-de81

NOVEL MECHANISMS FOR NITROGEN STORAGE, TRANSPORT, AND UPTAKE

2019· article· en· W6977184360 on OpenAlexfundno aff

Bibliographic record

VenueeCommons (Cornell University) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersBiological and Environmental ResearchDivision of Materials ResearchGreat Lakes Bioenergy Research CenterTowards Sustainability FoundationMaterials Research Science and Engineering Center, Harvard UniversityWestern Economic Diversification CanadaDavid R. Atkinson Center for a Sustainable Future , Cornell UniversityCornell Center for Materials ResearchCollege of Engineering, Michigan State UniversityOffice of ScienceMcKnight FoundationNational Research Council CanadaMichigan State UniversityU.S. Department of EnergyNatural Sciences and Engineering Research Council of CanadaCanadian Light SourceCanadian Institutes of Health ResearchNational Science Foundation
KeywordsBiocharCompostNutrientNitrogenOrganic matterEcosystemNutrient cycleLimiting

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.025
GPT teacher head0.172
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueeCommons (Cornell University)→Same topicSoil Carbon and Nitrogen Dynamics→French-language works237,207→