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Record W2178102873 · doi:10.1139/cjb-2015-0161

Sample size in studies on the germination process

2015· article· en· W2178102873 on OpenAlexvenueno aff
João Paulo Ribeiro‐Oliveira, Marli A. Ranal

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGerminationBiologyRobustness (evolution)Sample size determinationBotanyStatisticsHorticultureMathematics

Abstract

fetched live from OpenAlex

Studies on diaspore germination in native species with low economic relevance but great ecological significance have been based on a wide range of sample sizes. However, can the sample size change the physiological inferences made from germination measurements? To answer this question, diaspores of six Cerrado species were evaluated for germinability, germination time (initial, mean, and final), germination velocity (mean germination rate and Maguire’s rate), coefficient of variation of the germination time, and synchronization index of the germination process. Germinability, final time, mean time, and synchronization index were robust with respect to sample size fluctuation. Maguire’s rate, initial time, coefficient of variation of the germination time, and mean germination rate, in contrast, were affected by sample size fluctuation, at least in one of the species tested. The robustness of the time measurements and the synchronization index also demonstrates that the germination process occurs in a cadenced rhythm, much like a biological clock. Among the measurements evaluated, Maguire’s rate is the only one that must be avoided, since it is strongly influenced by sample size and by the balance between germinability and mean germination rate. These results demonstrate that sample size can affect inferences about the germination process and can compromise restoration and (or) conservation efforts.

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)0.001
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 score0.404
Threshold uncertainty score0.107

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.142
GPT teacher head0.286
Teacher spread0.144 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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