MétaCan
Menu
Back to cohort

Lab 4 Data - Quadrats and Bait Traps near Danby WOods

2014· dataset· en· W4394260728 on OpenAlexaboutno aff
Canyucel Gungor, Ranya Hasso, Roble Abdullahi

Bibliographic record

VenueFigshare · 2014
Typedataset
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuadratForestryGeographyEnvironmental scienceBiologyEcologyTransect

Abstract

fetched live from OpenAlex

This data was collected on two different days, on October 13, 2013 and October 20, 2013, from 2:30 pm to 5:30 pm. The location was the grasslands (Oct. 13) and the woodlots (Oct. 20) in and around Danby Woods, in the York University Campus.<br>On October 13, 2013, the weather was 16°C, and using the Beaufort Scale, the wind was determined to be mildly windy through the experiment. (Source: http://www.accuweather.com/en/ca/toronto/m5g/october-weather/55488?monyr=10/1/2014) On October 20, 2013, the weather was a cooler 12°C, and using the Beaufort Scale, the wind was determined to be mildly windy, growing windier as the experiment progressed. (Source: http://www.accuweather.com/en/ca/toronto/m5g/october-weather/55488?monyr=10/1/2014) For the grasslands (Oct. 13), quadrats were set up in low, medium and high disturbance areas, and data was recorded for high, medium and low disturbance areas. Each disturbance level had 10 quadrats repeated.Each quadrat was analyzed for roughly 3-4 minutes. Data regarding the area in the quadrat, such as grass length was recorded, along with the different animals that were captured. Bait traps containing sugar and cookies on a piece of paper towel were also set up in low, medium and high disturbance areas of the grasslands on the same day, and were left for about 1.5 hours. Each level of disturbance had 3 bait traps set up.The different animals that were in the bait trap were recorded.<br>Next week, on October 20th, quadrats and bait traps were set up in the woodlot in Danby Woods. Similar to the previous setup, areas of low, medium, and high disturbance were selected and data was recorded after analyzing the quadrat for 3-4 minutes. Each level of disturbance had 10 quadrats repeated in different locations. The data comprised of the insects found in the quadrat. On the same day, bait traps containing sugar on a piece of paper towel were set up in low, medium and high disturbance areas. Each bait tra was left for 1.5 hours, and each level of disturbance had 3 different bait traps set up. The different animals that were caught in the bait trap was recorded.

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), 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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.002

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 head0.059
GPT teacher head0.263
Teacher spread0.204 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicTree Root and Stability StudiesFrench-language works237,207