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Record W1970075324 · doi:10.2298/abs141017021j

Floristic and phytocoenological research of segetal plant communities in cultivated areas of southern Srem

2015· article· en· W1970075324 on OpenAlexaff
Snežana Jarić, Branko Karadzic, Sava Vrbnicanin, Miroslava Mitrović, Olga Kostić, Pavle Pavlović

Bibliographic record

VenueArchives of Biological Sciences · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany and Plant Ecology Studies
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsFloristicsPhytogeographyVegetation (pathology)GeographyPoaceaeFlora (microbiology)FabaceaeHabitatEcologyPlant ecologyCropTaxonomy (biology)BiologySpecies richnessTaxon

Abstract

fetched live from OpenAlex

Segetal vegetation was studied in the cultivated areas of southern Srem with the aim of analyzing its taxonomy, phytocoenology, syntaxonomy and phytogeography, as well as determining to what extent ecological factors influenced the differentiation of segetal plant communities among row crops, small grain crops and in alfalfa fields. Segetal flora was comprised of 124 plant species, classified into 38 families, of which Asteraceae (28), Fabaceae (10) and Poaceae (10) contained the greatest number of species. Three associations were selected based on phytocoenological analysis: Polygonetum convolvulo-avicularis, Consolido-Polygonetum avicularis and Lolio-Plantaginetum majoris, as well as five lower syntaxa (subassociations and facies). Crop type, moisture, habitat acidity (pH), temperature and anthropogenic factors had the greatest impact on the ecological differentiation of the studied vegetation. The significant presence of non-native species (18) was another consequence of the anthropogenic effects and geographic position of southern Srem, and these, as coenobionts of segetal plant communities and undesirable species, had a significant impact on crop yield.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
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 head0.230
GPT teacher head0.314
Teacher spread0.084 · 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.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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