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Record W4388022676 · doi:10.1029/2023av000990

Modeling Denitrification: Can We Report What We Don't Know?

2023· article· en· W4388022676 on OpenAlex
Balázs Grosz, Amanda Matson, Klaus Butterbach‐Bahl, Timothy J. Clough, Eric A. Davidson, René Dechow, S. DelGrosso, Efstathios Diamantopoulos, Peter Dörsch, Edwin Haas, Hongxing He, Christopher V. Henri, Dafeng Hui, Kristina Kleineidam, David Kraus, Matthias Kuhnert, Joël Léonard, Christoph Müller, Søren O. Petersen, Debjani Sihi, Iris Vogeler, Reinhard Well, Jagadeesh Yeluripati, Jinbo Zhang, Clemens Scheer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAGU Advances · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsMcGill University
FundersHorizon 2020 Framework ProgrammeBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftDeutscher Akademischer AustauschdienstEuropean Commission
KeywordsDenitrificationEnvironmental scienceComputer scienceChemistry

Abstract

fetched live from OpenAlex

Abstract Biogeochemical models simulate soil nitrogen (N) turnover and are often used to assess N losses through denitrification. Though models simulate a complete N budget, often only a subset of N pools/fluxes (i.e., N 2 O, , NH 3 , NO x ) are published since the full budget cannot be validated with measured data. Field studies rarely include full N balances, especially N 2 fluxes, which are difficult to quantify. Limiting publication of modeling results based on available field data represents a missed opportunity to improve the understanding of modeled processes. We propose that the modeler community support publication of all simulated N pools and processes in future studies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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