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Record W2776977274 · doi:10.1073/pnas.1708436115

Aridity weakens population-level effects of multiple species interactions on <i>Hibiscus meyeri</i>

2017· article· en· W2776977274 on OpenAlexfundno aff
Allison M. Louthan, Robert M. Pringle, Jacob R. Goheen, Todd M. Palmer, William F. Morris, Daniel F. Doak

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicHibiscus Plant Research Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaStrategic Environmental Research and Development ProgramUniversity of Colorado BoulderNational Science FoundationGovernment of CanadaUniversity of WyomingWyoming Space Grant Consortium
KeywordsPopulationBiologyAridHibiscusEcologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

(Malvaceae), across an aridity gradient in a semiarid African savanna. Population growth was more strongly affected by four distinct species interactions (competition with herbaceous and shrubby neighbors, herbivory, and pollination) in less stressful mesic areas than in more stressful arid sites. However, contrary to common assumptions, this effect did not arise because of greater density or diversity of interacting species in less stressful areas, but rather because aridity reduced sensitivity of population growth to these interactions. Our work supports classic predictions about the relative strength of factors regulating population growth across stress gradients, but suggests that this pattern results from a previously unappreciated mechanism that may apply to many species worldwide.

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 head0.303
GPT teacher head0.489
Teacher spread0.186 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

Explore more

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