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Record W4321445855 · doi:10.1080/11956860.2023.2180925

Non-native plants observed in North America by 18<sup>th</sup>century naturalists

2023· article· en· W4321445855 on OpenAlexvenueno aff
Carolyn A. Copenheaver, John A. Peterson, Kyrille DeBose, Jacob N. Barney

Bibliographic record

VenueEcoscience · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsNative plantFlora (microbiology)Introduced speciesNative americanInvasive speciesHuman settlementGeographyAgricultureEcologyBiologyEthnologyArchaeologyHistory

Abstract

fetched live from OpenAlex

Writings of 18th century naturalists provide a rich description of the flora, agricultural practices, and ecological and cultural landscape during the migration of large numbers of European and African peoples to North America. We employed a mixed methods approach to: 1) compare the percentages of non-native vs. native plant species recorded by the naturalists; 2) quantify the relative frequency of non-native plant species across eastern North America; and 3) qualitatively evaluate descriptions of non-native plants. The writings from nine naturalists in the 1700s document the introduction and establishment of many non-native plants across the North America. Higher proportions of non-native plant species were reported by naturalists who spent more time in densely populated human settlements. Agricultural crops had the highest relative frequencies for non-native plants in the 18th century. Non-native plants were described as being used during daily activities by humans, undergoing cultivation, growing abundantly on the landscape, and having weedy growth characteristics. Several of the non-native plants observed in the 1700s have subsequently developed into invasive species, which threaten the conservation of native North American flora.

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 score0.270
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.229
Teacher spread0.173 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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