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Record W7143676758 · doi:10.14988/0002000299

Geographic distribution of needle litter microfungi in British Columbia

2024· article· en· W7143676758 on OpenAlexaboutno aff
Shunsuke Matsuoka, Takashi Osono

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBiocrusts and Microbial Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofungiMoorlandLitterOrdinationLatitudeBiological dispersalSpatial distributionSpecies diversityBeta diversity

Abstract

fetched live from OpenAlex

The geographic distribution of microfungal diversity associated with needle litter was investigated in British Columbia, south-western Canada. A total of 77 microfungal species were isolated from needle litters of nine tree species in Pseudotsuga, Tsuga, Picea, and Abies collected in 25 coniferous forest sites that varied in climatic conditions and geographic locations. The nonmetric multidimensional scaling ordination showed the segregation of microfungal species composition between the study sites and needle species, which was significantly correlated to the latitude, elevation, mean annual temperature, and mean temperature at coldest and warmest months of the sites. Major microfungal species showed variable responses to these environmental factors: Trichoderma polysporum and Penicillium miczynskii tended to occur at higher elevations and latitudes and lower temperatures, compared with other species of the same genera. In contrast to the species composition, the mean number of species was not significantly affected by needle species, geographic locations, or climatic conditions. Applying variation partitioning to disentangle the relative effect of the environmental and spatial factors indicated the role of not only climatic but spatial factors in structuring fungal assemblages, suggesting the contribution of such non-niche processes as priority effect and dispersal limitation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.196
Teacher spread0.189 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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