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Record W4394400498 · doi:10.6084/m9.figshare.1194732

Distance-based Data Accumulation

2014· dataset· en· W4394400498 on OpenAlexaboutno aff
Prabhjot Benning

Bibliographic record

VenueFigshare · 2014
Typedataset
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

For the afternoon animal field experiment, taken place on Monday September 29, 2014, data was collected by a group of undergraduate students under the supervision of an experienced T.A. The experiment was situated throughout the York University Keele Campus’s grassland and woodlot and executed by sampling at random from approximately 2:45 PM - 5:15PM. The actual weather forecast (courtesy of http://www.accuweather.com/en/ca/toronto/m5g/september-weather/55488) for the allocated time varied anywhere from as low as 13ᵒC to as high as 24ᵒC. In addition, throughout the designated area there was a light breeze (according to the Beaufort scale) felt while carrying out the distance-based dataset. In each of the two areas (Grassland & Woodlot) a total of five transects were placed accordingly. Unlike the Grassland, we in particular utilized a belt transect method for the Woodlot given that it was a challenge to position the transects in a straight path. So consequently, a student had paced 50 footsteps (around 25 metres from the transects) along the specific path of the carefully positioned transects. For each and every trial achieved, we as a group had established the path along which the 50 foot paces were to be taken for the distance-based transects. Anything that was observed in that distance on either side of the path was accounted for in the accumulation of data. A pair of binoculars was used as an aid to further get a closer glimpse at the type of birds in-flight in an attempt to successfully classify the detected species by family name afterwards. Once the bird(s) became visible, we would subsequently estimate the projected distance (metres) the bird was sited from the transects. Soon after, with the assistance of the T.A. as well as the available bird illustrations guidebook to categorize the particular species found at each area, we as a group collectively reached upon a consensus and were able to identify and record each of the bird species in conjunction with its frequency of sighting(s). As for the wind speed in the woodlot, we used the Beaufort scale and chose the appropriate Seaman's terminology for the determination of the wind condition (we also asked the lab supervisor for confirmation on our assumption of the wind speed). Once again, this particular experiment was replicated at least five times within a specified vicinity of Grassland and Woodlot habitat.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, 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 score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0060.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.008

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 head0.173
GPT teacher head0.356
Teacher spread0.183 · 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

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