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Record W3128898613 · doi:10.1038/s41598-020-73441-1

Nickel excess affects phenology and reproductive attributes of Asterella wallichiana and Plagiochasma appendiculatum growing in natural habitats

2021· article· en· W3128898613 on OpenAlexaff
Anil Kumar Sharma, Madhulika Bhagat, Mohammad Urfan, Bilal Ahmed, Anima Langer, Villayat Ali, Dhiraj Vyas, Narendra Singh Yadav, Haroon Rashid Hakla, Shubham Sharma, Sikander Pal

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversity of Lethbridge
FundersUniversity Grants Commission
KeywordsHabitatPhenologyBiologyAbundance (ecology)EcologyPopulation

Abstract

fetched live from OpenAlex

Bryophytes are potent metal absorbers, thriving well on heavy metal (HM)-polluted soils. Mechanisms controlling uptake, compartmentalization and impacts of HMs on bryophytes life cycle are largely unknown. The current study is an effort to decipher mechanisms of nickel (Ni) excess-induced effects on the phenological events of two bryophytes, Asterella wallichiana and Plagiochasma apendiculatum growing in natural habitats. Observations revealed Ni-excess induced negative impacts on abundance, frequency of occurrence of reproductive organs, population viability and morphological traits, spore viability and physiological attributes of both the liverworts. Results led us conclude that P. appendiculatum survived better with the lowest impact on its life cycle events than A. wallichiana under Ni excess in natural habitats. Our findings collectively provide insights into the previously unknown mechanisms of Ni-induced responses in liverworts with respect to phenological attributes, as well as demonstrate the potential of P. appendiculatum to survive better in Ni excess habitats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractyes

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