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Record W3198723268 · doi:10.1139/cjb-2020-0233

Surviving or thriving? Trade-offs between growth, defense, and reproduction in a native versus an invasive <i>Rubus</i>

2021· article· en· W3198723268 on OpenAlexvenueno aff
Brady J. Thexton, Alex W. Bajcz

Bibliographic record

VenueBotany · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyRubusReproductionEcologyTrade-offLife history theoryBotanyLife history

Abstract

fetched live from OpenAlex

Little is known about limits to reproduction in plants, especially as to how their other life history functions (growth and defense) may constrain reproductive investment. Understanding these constraints can help researchers refine best practices for cultivating species like Rubus (Rosaceae) that produce nutritious fruits as well as for controlling invasive species. Here, we sought to elucidate potential trade-offs between growth, defense, and reproduction in native Rubus allegheniensis Porter (common blackberry) and invasive Rubus phoenicolasius Maxim. (wineberry) while accounting for the effects of varying insect herbivory and resource availability levels. We observed traits related to physical defense (e.g., prickle intensity), growth (e.g., cane length), and floral reproduction (e.g., ripe fruits), as well as carbon availability (e.g., canopy cover). We then used multiple regressions to characterize relationships between these variables for both species. We found potential evidence for 2 induced defenses in the invasive wineberry. Also, 5 models returned significant results indicative of trade-offs between reproduction and growth, reproduction and defense, and defense and growth in a single or both species. Our results highlight the importance of understanding the defensive strategies utilized by these species because inducible defenses may result in trade-offs that could reduce yields and (or) increase the invasive potential of Rubus species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.072
GPT teacher head0.283
Teacher spread0.211 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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