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Record W2334614853 · doi:10.1139/cjb-2012-0274

Rarity and reproductive biology: habitat specialists reveal a complex relationship

2013· article· en· W2334614853 on OpenAlexvenueno aff
Nicole E. Miller‐Struttmann

Bibliographic record

VenueBotany · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPollinationHabitatEcologyPollinatorReproductive successPollenReproductive isolationFecundityRange (aeronautics)Population

Abstract

fetched live from OpenAlex

Understanding how species with historically fragmented populations are able to persist will provide insights into which factors may be important for the maintenance of newly fragmented populations. Plants with fragmented and isolated populations, such as habitat-specialist (HS) species, are likely less attractive to pollinators and may have adaptive traits that compensate for these distributional challenges, such as larger flowers and more specialized pollination systems. If they do not have these adaptations, HS species are predicted to have lower reproductive success and be more pollen limited than widespread species. Here, I test three predictions concerning differences in reproductive traits that are known to affect attractiveness to pollinators, pollen receipt, and reproductive success, by comparing three HS species to congeneric species with broader habitat use (HT, habitat tolerators). Two of the three HS species lend partial support to the predictions that HS species have larger floral displays and more specialized pollination systems. The third HS species did not have either of these traits but did have lower relative seedset compared to its matched HT. These results provide preliminary support for a positive relationship between habitat specificity and pollination specialization, and for the role of low fecundity in contributing to range restriction of HS 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)0.000
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 score0.212
Threshold uncertainty score0.189

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)0.0000.000

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 head0.103
GPT teacher head0.250
Teacher spread0.148 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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