YorkU.Forest.Oct5-2016.csv - Census 1: Observations in a Forest
Bibliographic record
Abstract
YORK UNIVERSITY - WEEK 5 FOREST - METADATA Overview:Purpose:The purpose of this lab was to identify and measure the abundance and diversity of herbaceous plants, woody plants, vertebrates, and invertebrates in a forested area near York University.Observations:The observations were made in four separate sections by four individual groups in parallel: one for observing herbaceous plants, one for woody plants, one for vertebrates, and one for invertebrates. This metadata is separated into four sections accordingly.Location:Observations were made in a woodlot north of York University’s Maloca Community Garden. This is located on the southwest corner of the Keele Campus in Toronto, Ontario, Canada. Longitude and latitude: TBD.Time:Observations were conducted from approximately 3:00PM to 4:45PM EST for a total of a 105 minutes on Wednesday October 5, 2016.Conditions:The lab was conducted on a sunny day with temperatures around 19°C. No clouds were seen. Data Collectors (Lab 08 Group 01):Kathleen Gatdula, Yaakov Green, Christina Leung, Mariam Maasarany, Nawang Yanga Due to limitations on the length of this description, metadata can be found on the PDF attachment.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.577 | 0.397 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".