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Record W6911169036 · doi:10.5063/f1gt5kdc

Long-term ecological vegetation data for Carrizo National Monument, California

2018· dataset· en· W6911169036 on OpenAlexaff

Bibliographic record

VenueUC Santa Barbara · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsQuadratShrubVegetation (pathology)CanopyAbundance (ecology)National parkRelative species abundance

Abstract

fetched live from OpenAlex

Vegetation dynamics were censused at peak flowering in March to April from 2015 to 2018 at Carrizo National Monument. A total of 4 distinct sites were used to monitor vegetation. The shrub species Ephedra californica was used as the foundation species to structure shrub-open contrasts annuals. A paired shrub-open contrast using 0.5 x 0.5m quadrats were deployed under the shrub canopy and at least 1m at open microsites defined as adjacent sites without canopy cover by a dominant plant species. A total of 1194 independent shrubs were used (and the same number of open microsites), and repeated-measures were avoided by randomly selecting sites at each site each year using transects. For each shrub, the height, longest dimension in width, and perpendicular width was measured, and the volume for a sphere was used to summarize shrub sizes in subsequent analyses. The total abundance of each annual species present was recorded within plots. All analyses to support these data including quality assessment and control were done in R version 3.5.1 and published at Zenodo: Lortie, C.J. 2018. Carrizo long-term ecological data analyses. Zenodo: DOI: 10.5281/zenodo.1455808.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.019

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 head0.071
GPT teacher head0.357
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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