MétaCan
Menu
Back to cohort
Record W2896482726 · doi:10.1080/11956860.2018.1530327

Effect of soil properties and vegetation characteristics in determining the frequency of Burgundy truffle fruiting bodies in Southern Poland

2018· article· en· W2896482726 on OpenAlexvenueno aff
Dorota Hilszczańska, Aleksandra Rosa-Gruszecka, Radosław Gawryś, Jakub Horák

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsTruffleAbiotic componentBiologyVegetation (pathology)BotanyDeciduousEcologySymbiosis

Abstract

fetched live from OpenAlex

The Burgundy truffle (Tuber aestivum Vittad.) has a wide-ranging distribution across Europe, yet its ecology is far from being well understood. For instance, although the literature on the ecophysiology of this species is dominated by the symbiosis with deciduous hosts, the real range of hosts in nature seems to be much wider than the current distribution of T. aestivum. The aim of this study was to determine the relative importance of abiotic (soil) and biotic (vegetation) properties in determining the performance of T. aestivum in this pioneering stage of research on truffles in Poland. Soil parameters influenced the formation of T. aestivum fruiting bodies more strongly than plant composition. The number of fruiting bodies increased with increasing concentration of soil calcium and phosphorus. The number of plant species was the only significant predictor among the investigated vegetation characteristics. The influence of this predictor was positive, as an increasing number of fruiting bodies was found when the number of plant species was higher. The presence of truffle fruiting bodies was significantly correlated with the presence of five plant species, viz.: Brachypodium sylvaticum, Cephalanthera damasonium, Cornus sanguinea, Sanicula europaea and Viola mirabilis.

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 score0.199
Threshold uncertainty score0.252

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 head0.017
GPT teacher head0.216
Teacher spread0.200 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEcoscienceSame topicMycorrhizal Fungi and Plant InteractionsFrench-language works237,207