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Glasshouse vs field experiments: do they yield ecologically similar results for assessing N impacts on peat mosses?

2012· article· en· W2128154130 on OpenAlexaff
Juul Limpens, Gustaf Granath, Rien Aerts, Monique Heijmans, Lucy J. Sheppard, Luca Bragazza, B. L. Williams, Håkan Rydin, Jill L. Bubier, Tim R. Moore, Line Rochefort, Edward A. D. Mitchell, Alexandre Buttler, Leon J.L. van den Berg, Urban Gunnarsson, André‐Jean Francez, Renato Gerdol, Markus N. Thormann, P. Grosvernier, Magdalena M. Wiedermann, Mats B. Nilsson, Marcel R. Hoosbeek, Suzanne E. Bayley, J. ‐F. Nordbakken, M.P.C.P. Paulissen, Stefan Hotes, Angela Breeuwer, Mati Ilomets, H.B.M. Tomassen, Ian D. Leith, Bin Xu

Bibliographic record

VenueNew Phytologist · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of AlbertaUniversité LavalMcGill University
FundersNatural Environment Research CouncilWageningen University and ResearchUppsala Universitet
KeywordsPeatYield (engineering)Environmental scienceField (mathematics)GreenhouseEcologyBiologyGeographyBotanyMathematicsPhysics

Abstract

fetched live from OpenAlex

• Peat bogs have accumulated more atmospheric carbon (C) than any other terrestrial ecosystem today. Most of this C is associated with peat moss (Sphagnum) litter. Atmospheric nitrogen (N) deposition can decrease Sphagnum production, compromising the C sequestration capacity of peat bogs. The mechanisms underlying the reduced production are uncertain, necessitating multifactorial experiments. • We investigated whether glasshouse experiments are reliable proxies for field experiments for assessing interactions between N deposition and environment as controls on Sphagnum N concentration and production. We performed a meta-analysis over 115 glasshouse experiments and 107 field experiments. • We found that glasshouse and field experiments gave similar qualitative and quantitative estimates of changes in Sphagnum N concentration in response to N application. However, glasshouse-based estimates of changes in production--even qualitative assessments-- diverged from field experiments owing to a stronger N effect on production response in absence of vascular plants in the glasshouse, and a weaker N effect on production response in presence of vascular plants compared to field experiments. • Thus, although we need glasshouse experiments to study how interacting environmental factors affect the response of Sphagnum to increased N deposition, we need field experiments to properly quantify these effects.

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)0.001
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 score0.169
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.045
GPT teacher head0.313
Teacher spread0.268 · 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

Citations59
Published2012
Admission routes1
Has abstractyes

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