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Carbon and nitrogen elemental and isotopic patterns in macrofungal sporocarps and trees in semiarid forests of the south‐western USA

2005· article· en· W2032554683 on OpenAlexfundno aff
Stephen C. Hart, Catherine A. Gehring, Paul C. Selmants, Ron J. Deckert

Bibliographic record

VenueFunctional Ecology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Geological SurveyU.S. Environmental Protection AgencyNational Science Foundation
KeywordsTrophic levelBiologyδ13Cδ15NEcologyTemperate rainforestUnderstoryTemperate climateCoarse woody debrisIsotopes of nitrogenIsotope analysisEcosystemBotanyStable isotope ratioHabitat

Abstract

fetched live from OpenAlex

Summary Previous studies in humid forests have shown that the 13 C and 15 N isotopic composition differs between ectomycorrhizal (ECM) and saprotrophic (SAP) fungi, and that this ECM–SAP ‘divide’ may provide a useful tool for evaluating fungal trophic status. We evaluated whether this method could delineate the trophic status of fungi in two semiarid, temperate forests of the south‐western USA. This technique could be particularly valuable in arid regions where the functional roles of fungi can be difficult to assess because of infrequent sporocarp production. Our data were consistent with the existence of an ECM–SAP divide, although δ 13 C values were more useful than δ 15 N values in separating trophic status. Saprotrophic fungi consistently had higher δ 13 C values than their presumed substrates; however, the degree of 15 N enrichment in SAP sporocarps was highly variable. Comparison of 11 sporocarp species common to both sites showed that δ 15 N values were higher in one of the forests, even though the δ 15 N values of foliage from common understorey and overstorey trees were similar between forests. We conclude that assessment of the isotopic compositions of fungal sporocarps and their substrates is helpful for elucidating ecological relationships in semiarid 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.652

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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
Published2005
Admission routes1
Has abstractyes

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