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Record W6950712138 · doi:10.5683/sp2/7dlo5q

Data from: Plant diversity and endemism in the California Floristic Province

2021· dataset· en· W6950712138 on OpenAlexaff

Bibliographic record

VenueBorealis · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndemismFloristicsVascular plantPlant diversityBiodiversityTaxonPlant communityFlora (microbiology)

Abstract

fetched live from OpenAlex

AbstractThe California Floristic Province (CFP) is an area of high biodiversity and endemism corresponding roughly to the portion of western North America having a Mediterranean-type climate. High levels of diversity and endemism in the CFP are attributed to the unique geo-climatic setting of the region. In recent years, much has been learned about the origins of plant diversity in western North America. This work, however, has been hindered by a focus on political rather than biotic regions, such that much more is known about diversity and endemism in the state of California than the natural biotic region represented by the CFP. Here we present a preliminary list of native land plants (vascular plants and bryophytes) found in the CFP, as well as an analysis of diversity and endemism patterns at the level of both species and minimum rank taxa (MRT; species and infraspecific taxa). A total of 6,927 MRT are native to the CFP, including 6,143 vascular plants and 784 bryophytes. Of these, 2,612 vascular plants are endemic to the CFP (42%) compared to 37 endemic bryophytes (5%). Finally, 2,506 native CFP vascular plant MRT (41% of the CFP flora) and 454 CFP bryophyte MRT (58% of the CFP flora) are found outside California in the Oregon and Baja California parts of the CFP. This high degree of sharing across political boundaries among both vascular plants and bryophytes highlights the cohesiveness of the CFP, and the need to focus more research effort on biotic regions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.205
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.270
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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