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Record W2415664819 · doi:10.1139/cjb-2015-0263

Net nitrogen mineralization in boreal fens: a potential performance indicator for peatland reclamation

2016· article· en· W2415664819 on OpenAlexfundvenueaboutno aff
Jeremy A. Hartsock, Melissa House, Dale H. Vitt

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersSyncrude
KeywordsSandhillPeatBorealEnvironmental scienceNitrogen cycleNitrificationMineralization (soil science)Land reclamationWetlandHistosolEcologySoil waterEnvironmental chemistryAgronomyNitrogenBiologyChemistrySoil organic matterSoil scienceHabitat

Abstract

fetched live from OpenAlex

The Sandhill Fen reclamation watershed, commissioned by Syncrude Canada Ltd., is the first attempt to reclaim a self-sustaining peat-forming wetland on a previously mined area. Here, we quantified net nitrogen mineralization rates at Sandhill Fen in the first and second years since initiation (2013–2014). Our main objective was to determine whether nitrogen production potentials at Sandhill Fen were similar to six regional fens sampled across an ombrotrophic–minerotrophic peatland gradient. In the second year, net nitrogen mineralization rates across Sandhill Fen (2014 mean = 20.2 mg N·m −2 ·day −1 ; 0.9 mg N·kg −1 ·day −1 ) were quite comparable with the benchmark fen sites (2013–2014 pooled means = 20.6 mg N·m −2 ·day −1 ; 5.9 mg N·kg −1 ·day −1 ). However, in areas exhibiting low gravimetric soil moisture content at Sandhill Fen, net nitrification contributed more than 50% to the net N mineralization total, an uncommon observation in natural fen type wetlands. These findings highlight the importance of managing soil moisture levels during the early stages of reclamation to (1) maintain relatively anaerobic soil conditions, and (2) facilitate microbial-mediated processes to fall within an acceptable range of variation comparable to undisturbed Albertan fens.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.286

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2016
Admission routes3
Has abstractyes

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