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Record W1892258668 · doi:10.1139/cjb-2013-0170

Valuing native ectomycorrhizal fungi as a Mediterranean forestry component for sustainable and innovative solutions

2014· article· en· W1892258668 on OpenAlexvenueno aff
Anabela Marisa Azul, João Nunes, Inês Ferreira, Ana Coelho, Paula Verı́ssimo, João Trovão, António Campos, Paula Castro, Helena Freitas

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversitySustainabilityEcosystemContext (archaeology)AgroforestryForest ecologyEcosystem servicesBusinessProductivityBiologyEnvironmental resource managementEcologyEnvironmental scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Native ectomycorrhizal fungi (ECMF) represent an emergent critical support in forestry and bioindustry while providing an attractive economical return and ecosystems services. These attributes are desirable given the human activities that are affecting ecosystems and biodiversity worldwide. The Mediterranean region has inherited native forests that are in serious decay, with serious environmental and socioeconomic consequences as a result of human influence in shaping ecosystems, particularly over the last century. In this context, edible ECMF are important not only because of their value for ecosystem functions, but also for their organoleptic and nutritive properties, and because of the presence of bioactive compounds. In this paper, we discuss critical aspects of ECMF diversity and traits for forest health, productivity, and sustainability, as well as the importance of exploring biologically active proteins obtained from native ECMF as sources for future forest management planning and industry innovation. The use of convergent approaches to ameliorate the identity of ECMF reservoirs in forest ecosystems and rural lands is urgently required to restore and protect native biodiversity and ecosystems services and meet efficient production solutions to provide sustainable innovation while ensuring environmental safety.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.689

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 studies0.0010.000
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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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 designNot applicable
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

Citations32
Published2014
Admission routes1
Has abstractyes

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