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Record W1989555074 · doi:10.1139/b09-021

Effects of shading on photosynthesis, plant organic nitrogen uptake, and root fungal colonization in a subarctic mire ecosystem

2009· article· en· W1989555074 on OpenAlexvenueno aff
Maria Olsrud, Anders Michelsen

Bibliographic record

VenueBotany · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
FundersSvenska Forskningsrådet Formas
KeywordsBiologyEcosystemShrubSubarctic climateBotanyPhotosynthesisShadingEvergreenColonizationEcology

Abstract

fetched live from OpenAlex

Arctic dwarf shrub ecosystems are predicted to be exposed to lower light intensity in a changing climate where mountain birch forests are expanding. We investigated how shading at 0%, 65%, and 97% affects photosynthesis, organic N uptake, C and N allocation patterns in plants, and root fungal colonization in an ericoid dwarf shrub ecosystem. The ecosystem was labeled by injection of [2- 13 C, 15 N]glycine into the soil, and the uptake of 15 N and 13 C in roots and leaves 24 h later was analysed. Fungal colonization in hair roots was determined visually. Hair root 13 C: 15 N ratios showed that dwarf shrub ecosystems are capable of taking up organic N as intact glycine both under high irradiance levels and under shaded conditions when photosynthesis is strongly reduced. The allocation of 15 N to green leaves of Rubus chamaemorus L. increased with shading, whereas the allocation of 13 C to leaves of both deciduous and evergreen plant species decreased. Species dominance was correlated with uptake of 13 C, i.e., the most productive species also took up the highest amount of glycine. The ecosystem exhibited a tendency towards lower colonization by ericoid mycorrhizal fungi and dark septate endophytes in hair roots when shaded. Thus, shading has implications for processes central to both C and N cycling in subarctic ecosystems. This should be considered in projections of ecosystem responses to climate change and expanding mountain birch forests.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.256

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 head0.007
GPT teacher head0.184
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations21
Published2009
Admission routes1
Has abstractyes

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