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Record W7157885368 · doi:10.5886/dn5lei

Vegetation composition of prairie creation, old-field, and meadow sites in Hamilton, Ontario (2020)

2020· dataset· en· W7157885368 on OpenAlexaffabout
Sebastian Irazuzta, Noah Stegman

Bibliographic record

VenueCanadensys · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsMcMaster University
Fundersnot available
KeywordsQuadratVegetation (pathology)Sampling (signal processing)Vegetation coverPlant coverTable (database)

Abstract

fetched live from OpenAlex

Vegetation composition was surveyed in early June 2020 at five sites in the Hamilton, Ontario region, Canada. Surveys were conducted within fixed 50 x 50 m plots corresponding spatially to standardized bee survey locations sampled between 2014 and 2016. Vegetation was sampled using replicated 1 m2 quadrats randomly selected within each plot, with 15 quadrats per site and 75 quadrats in total. All vascular plants and bryophytes within quadrats were identified to species or morphospecies, and percent cover was estimated using the Pfister cover class scale. Additional plant species observed within survey plots but outside quadrats were also recorded to supplement site level species richness. For data publication, percent cover values are reported as midpoint estimates derived from Pfister cover class bins. The dataset provides standardized site level vegetation composition and structure following the completion of bee sampling and represents realized vegetation state at the time of survey.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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.

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
Published2020
Admission routes2
Has abstractyes

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