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Record W2550002743 · doi:10.1139/cjb-2016-0117

Variation in photosynthetic properties among bog plants

2016· article· en· W2550002743 on OpenAlexvenueno aff
Aino Korrensalo, Tomáš Hájek, Timo Vesala, Lauri Mehtätalo, Eeva‐Stiina Tuittila

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersAcademy of FinlandItä-Suomen Yliopisto
KeywordsBiologyBogEvergreenPhotosynthesisBotanyPhotosynthetic capacitySphagnumInterspecific competitionEcologyPeat

Abstract

fetched live from OpenAlex

Plant functional types (PFTs) are used to make generalizations in modeling how plants impact ecosystem functioning. In boreal bogs the number of plant species is small, but several PFTs are represented, namely sedges, deciduous and evergreen dwarf-shrubs, as well as hummock, lawn, and hollow Sphagna. Despite the use of PFTs in modeling, the value of PFTs to describe the photosynthetic properties of bog plants has not been systematically studied. We aim to quantify the photosynthetic properties of typical bog plant species and assess how well PFT divisions reflect differences among species. We measured photosynthetic light response and physiological state of photosystem II of 19 species, monthly, over a growing season. Differences were assessed using principal component analysis and mixed models. Photosynthetic properties separated Sphagna into traditional PFTs, of which hollow species had the highest gross photosynthesis. Sphagnum photosynthesis had large seasonal variation, as monthly differences exceeded those among PFTs or species. The photosynthetic properties of vascular plants differed widely among species but did not follow traditional PFTs. Vascular plant seasonal changes were of less importance than interspecific differences. The results justify using PFTs to describe the ability of bog Sphagna to bind carbon, but do not justify the same approach for vascular plants.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
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 communication0.0000.000
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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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.

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

Citations33
Published2016
Admission routes1
Has abstractyes

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