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Record W4307581248 · doi:10.1093/jxb/erac432

Bulb growth potential is independent of leaf longevity for the spring ephemeral <i>Erythronium americanum</i> Ker-Gawl.

2022· article· en· W4307581248 on OpenAlexafffund
Hugo Bertrand, Line Lapointe

Bibliographic record

VenueJournal of Experimental Botany · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyBulbPhotosynthesisBotanyHorticulture

Abstract

fetched live from OpenAlex

Growth in most spring ephemerals is decreased under warmer temperatures. Although photosynthetic activities are improved at warmer temperatures, leaves senesce earlier, which prevents the bulb from reaching a larger size. A longer leaf life duration during a warm spring, therefore, may improve bulb mass. We tested this hypothesis by modulating leaf life span of Erythronium americanum through the application of Promalin® (PRO; cytokinins and gibberellins) that prolonged or silver thiosulfate (STS) that reduced leaf duration. Gas exchange and chlorophyll fluorescence were measured along with leaf and bulb carbohydrate concentrations. Plants were also pulse labelled with 13CO2 to monitor sugar transport to the bulb. Lower photosynthetic rates and shorter leaf life span of STS plants reduced the amount of carbon that they assimilated during the season, resulting in a smaller bulb compared with control plants. PRO plants maintained their photosynthetic rates for a longer period than control plants, yet final bulb biomass did not differ between them. We conclude that seasonal growth for E. americanum is not limited by leaf life duration under warm growing conditions, but rather by limited sink growth capacity. Under global warming, spring geophytes might be at risk of being reduced in size and, eventually, reproducing less frequently.

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 categoriesInsufficient payload (model declined to judge)
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 score0.168
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.221
Teacher spread0.213 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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