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Record W2155850696 · doi:10.1139/b00-150

Isozyme variation and its environmental correlates in<i>Elymus glaucus</i>from the California Floristic Province

2001· article· en· W2155850696 on OpenAlexvenueno aff
Barbara L. Wilson, Jay H. Kitzmiller, Wayne Rolle, Valerie D. Hipkins

Bibliographic record

VenueCanadian Journal of Botany · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
FundersUniversity of California, DavisU.S. Department of Agriculture
KeywordsBiologyElymusBotanyGenetic variationGenetic variabilityGenetic distanceIntraspecific competitionPolyploidEcologyPoaceaePloidy

Abstract

fetched live from OpenAlex

Genetic variation in the self-fertile, allotetraploid grass Elymus glaucus Buckley was assessed using isozymes in 133 populations from southwestern Oregon and from the San Francisco Bay area and central Sierra Nevada mountains in California. Elymus glaucus was highly (98.5%) homozygous but also highly variable; 77% of loci were polymorphic, and the mean number of alleles per locus was 2.96. Populations were highly differentiated, with 40% of variation among populations. Geographic and genetic distances among populations were not correlated, except that populations collected within 5 km were generally more similar than average. Genetic distance among populations could not be predicted from geographic distance, geographic location, foliage pubescence, serpentine substrate, or habitat moisture. However, two genetic clusters, associated with elevation, did emerge. The taxonomic status of Elymus glaucus ssp. jepsonii (Burtt Davy) Gould, based on leaf pubescence, was not supported.Key words: Elymus glaucus, isozyme, genetic variation, Poaceae, polyploid, seed transfer.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.151
Teacher spread0.142 · 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
Published2001
Admission routes1
Has abstractyes

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