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Record W4394137658 · doi:10.6084/m9.figshare.16806949

Backyard Bird Observation Experiment

2021· dataset· en· W4394137658 on OpenAlexaboutno aff
Wong

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Data with meta-data for a backyard birdwatching experiment done as part of course BIOL3250 at York University, Canada. Descriptions of Table:Replicate represents the replicate or unique animal observed.Date is the date the observation was made in the format d/m/y.Researcher is the initials of the person who inputted the observational data. Location is the broad address of where the data was collected. It includes the name of the neighbourhood and the city it is found in.Species represents the common name of the bird species identified.Frequency is the number of times the species of bird was observed performing a specific behaviour during the 60-minute sampling period.Behaviour is a brief description of the activity the bird was seen doing such as flying, perching on a tree, or interacting with other birds.Duration represents the total amount of time the bird spent on a certain behavior during the sampling period.Noise is if the birds made any noise such as chirping or cawing during the time they were seen doing a behavior. Each row or replicate in the table has a single species of bird per behavior. There is a unique combination of species of bird with behavior in each row as there is one species per behavior per row. The observational study was conducted over a 60-minute period on a single day at 8:30am. The weather during the experiment was clear and sunny with little wind.

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)0.003
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.009

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 head0.060
GPT teacher head0.280
Teacher spread0.220 · 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
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
Published2021
Admission routes1
Has abstractyes

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