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Record W4211142086 · doi:10.3897/neobiota.71.76573

Using leaf functional traits to remotely detect Cytisus scoparius (Linnaeus) Link in endangered savannahs

2022· article· en· W4211142086 on OpenAlexafffund
Paul W. Hacker, Nicholas C. Coops

Bibliographic record

VenueNeoBiota · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBroomBiologyEndangered speciesWoodlandGrasslandBiodiversityBotanyCanopyEcologyHabitat

Abstract

fetched live from OpenAlex

Identification of invasive plant species must be accurate and timely for management practices to be successful. Currently, Cytisus scoparius (Scotch broom) is expanding unmonitored across North America’s west coast, threatening established ecological processes and altering biodiversity. Remote detection of leaf functional traits presents opportunities to better understand the distribution of C. scoparius. This paper demonstrates the capacity for remotely sensed leaf functional traits to differentiate C. scoparius from other common plant species found in mixed grassland-woodland ecosystems at the leaf- and canopy-levels. Retrieval of leaf nitrogen percent, specifically, was found to be significantly higher in C. scoparius than each of the other 22 species sampled. These findings suggest that it may be possible to accurately detect introduced C. scoparius individuals using information collected from leaf and imaging spectroscopy at fine spatial resolutions.

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 score0.005
Threshold uncertainty score0.010

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 head0.074
GPT teacher head0.276
Teacher spread0.202 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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