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Record W4396894512 · doi:10.1101/2024.05.10.593301

Effects of goldenrod ( <i>Solidago gigantea</i> Aiton and S. <i>canadensis</i> L.) invasion on the wet meadow vegetation composition of Somogyfajsz wood pasture

2024· preprint· en· W4396894512 on OpenAlexaboutno aff
Katalin Szitár, Melinda Kabai, Zita Zimmermann, Gábor Szabó, Bruna Paolinelli Reis, László Somay

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicBotany and Plant Ecology Studies
Canadian institutionsnot available
FundersNational Research, Development and Innovation OfficeNemzeti Kutatási Fejlesztési és Innovációs Hivatal
KeywordsSolidago canadensisVegetation (pathology)PastureComposition (language)BiologyBotanyEcologyInvasive speciesMedicineArt

Abstract

fetched live from OpenAlex

Abstract In recent decades, restoring abandoned wood pastures are being rediscovered as an effective way to preserve biodiversity. Their restoration include the control of invasive plant species. In our study, we compared the vegetation composition of wet meadow patches uninvaded and invaded by giant and Canadian goldenrod in a wood pasture located in Somogyfajsz, Hungary. We sampled the vegetation of the wet meadows of the pasture in 16 pairs of 1 x 1 m invaded and uninvaded quadrats in June 2020. We studied the effect of goldenrod invasion on nature conservation value in terms of species diversity, origin of species, and Social Behaviour Types, and forage quality and quantity. The study revealed that goldenrod invasion had negative effects on species richness and community-level biomass in wet meadows, leading to a significant reduction in species diversity and total cover. Goldenrod invasion also impacted the richness and cover of both native and exotic species, with a decrease in richness but not cover, highlighting the competitive ability of goldenrods and their potential to alter vegetation composition.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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