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Record W2329381719 · doi:10.1139/cjb-2013-0198

Ectomycorrhizal fungi and the nitrogen economy of conifers — implications for genecology and climate change mitigation

2014· article· en· W2329381719 on OpenAlexafffundvenue
J. M. Kranabetter

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsMinistry of ForestsGovernment of British Columbia
FundersMinistry of Forests, Lands and Natural Resource Operations
KeywordsClimate changeBiologyEcologyHost (biology)Range (aeronautics)Soil fertilityAgroforestrySoil water

Abstract

fetched live from OpenAlex

The nitrogen (N) economy of conifers is hypothesized to reflect three spatially defined and interacting sources of variability in forest nutrition. These include the physiological adaptations of the host tree (N uptake capacities among populations), matched to the particular amount and nature of soil N supply (organic N, NH 4 + , and NO 3 – ), as mediated by communities of site-adapted ectomycorrhizal (EM) fungi. The spatial attributes of an N economy may vary considerably over the ranges of tree species because of wide gradients in climate and soil fertility, underpinning a potentially important aspect of conifer genecology with implications for climate change mitigation. The evidence for an intersection of N supply with host demand, as mediated by EM fungi, will be briefly reviewed and then evaluated in light of assisted migration studies involving provenance trials of coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) in southwestern British Columbia. The trials were established across a wide range of site types, and so they provide valuable data on host response to gradations in soil N supply and interactions with local EM fungal communities. Preliminary results and knowledge gaps will be discussed under the framework of an N economy and management of forest genetic resources.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.122

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations21
Published2014
Admission routes3
Has abstractyes

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