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Record W2069651478 · doi:10.1139/x10-004

Cold-season nitrous oxide dynamics in a drained boreal peatland differ depending on land-use practice

2010· article· en· W2069651478 on OpenAlexvenueno aff
Marja Maljanen, Jyrki Hytönen, Pertti J. Martikainen

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersAcademy of Finland
KeywordsPeatNitrous oxideEnvironmental scienceSoil waterSoil respirationGreenhouse gasCarbon dioxideTaigaGrowing seasonSink (geography)MethaneSoil carbonAgronomyAnimal scienceChemistrySoil scienceEcologyBiology

Abstract

fetched live from OpenAlex

Drained peat soils are important sources of greenhouse gases such as nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2). These gases are produced in soil and they can be emitted year-round. We measured N2O and CH4 flux rates and total respiration (RTOT) over a year from a drained peatland with one subsite as a grass field and another forested. The field acted annually as a small source (0.36 ± 0.73 kg C·ha–1) and the forest as a sink (–1.93 ± 0.50 kg C·ha–1) for CH4. Mean annual RTOT rates were 660 and 297 mg·m–2·h–1 in the field and in the forest, respectively. Annual N2O emission rates were 34.8 ± 2.4 kg N·ha–1 from the field and 25.5 ± 5.5 kg N·ha–1 from the forest. More than 80% of the annual N2O emissions took place during winter. In the field, high emissions were detected during thawing in April when N2O accumulated in soil during the winter was released. In the forest, N2O emissions peaked when the top soil was freezing in January and accumulation of N2O in soil was less than in the field. The timing of the episodic high N2O emissions thus differed depending on the land use.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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 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

Citations34
Published2010
Admission routes1
Has abstractyes

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