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Lab 2: Field training with plants (transects)

2014· article· en· W2424425176 on OpenAlexaboutno aff
Akani Chioma

Bibliographic record

VenueFigshare · 2014
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)TransectField (mathematics)Field trainingEnvironmental scienceGeographyEngineeringBiologyMathematicsEcologyOperations managementMeteorology

Abstract

fetched live from OpenAlex

The experiment was performed at grassland located on the Keele campus of York University. It was conducted by a group of four students on the 24th of September 2014 from approximately 3:15pm to 3:45pm and it was a sunny day with temperature between 20°C to 25°C. Firstly, a visible plant species was observed and recorded then a transect tape was randomly placed in a location where the target plant species was found. Along the transect tape, individual target plant species were observed and their distance on the tape in meters were recorded. The height in meters of each plant was measured using another transect tape. The number of leaves and number of flowers on the plant was estimated for each plant sample. The level of crowding was obtained by determining whether or not the plant species was crowded by other plants (in scale of 0 to 3) and the estimation was made by looking at both sides of the transect tape. The data was collected for 20 individual plants of the species Canadian Goldenrod (Solidago Canadensis).

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0310.010

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 head0.088
GPT teacher head0.307
Teacher spread0.218 · 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
GenreOther

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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