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Record W2619788443 · doi:10.1134/s1067413617030080

Effect of temperature and precipitation on linear increment of Sphagnum fuscum and S. magellanicum in Western Siberia

2017· article· en· W2619788443 on OpenAlexaff
Natalia P. Kosykh, Natalia G. Koronatova, Gustaf Granath

Bibliographic record

VenueRussian Journal of Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcMaster University
FundersRussian Foundation for Basic Research
KeywordsTransectTundraPrecipitationBogEnvironmental scienceEcologyPhysical geographyGeographyPeatEcosystemBiology

Abstract

fetched live from OpenAlex

The linear increment of Sphagnum fuscum and S. magellanicum in ombrotrophic mires of Western Siberia has been measured during two years over a transect about 2500 km long extending from forest–steppe to forest–tundra. Along the latitudinal gradient, the increment of both species has proved to be correlated with annual average air temperature and, in S. magellanicum , also with annual precipitation. The determinants of their growth differ between the southern, central, and northern parts of the study region. At the regional level, the annual and summer precipitation plays a more important role than the average air temperature. The increment of S. fuscum in the southern part is positively correlated with the amount of precipitation and negatively correlated with summer temperature, whereas the situation in the central part is inverse. In S. magellanicum , the linear increment is directly dependent on the annual average temperature and annual and summer precipitation in the south and on the annual and summer precipitation in the north of Western Siberia. The dynamics of linear growth of both species in bog pine forests during the growing season are similar: its rate is the highest in June, when the linear increment of S. fuscum and S. magellanicum amounts to 60 and 85% of the annual total, respectively.

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)0.001
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 score0.005
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.252
Teacher spread0.247 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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