Bibliographic record
Abstract
For this afternoon field experiment, held on Monday October 27, 2014, data was collected throughout the York University Keele Campus’s woodlot from approximately 2:45PM - 5:00PM. The actual weather forecast (courtesy of http://www.accuweather.com/en/ca/toronto/m5g/october-weather/55488) for that time of day varied anywhere from as low as 1ᵒC to as high as 12ᵒC. The execution component of the group designed experiment consisted of four males, one of which included myself. All four team members worked collectively as a group to conduct the experiment and collect the appropriate data (low grassland v.s. high grassland) so as to ensure the most accurate results. To conduct this experiment one needs to gather all of the required materials (pens/pencils plus lined paper for tabulation of results, a line transect along with a sweep net, and quite possibly an insect dichotomous key) beforehand. Make sure to assign two distinct areas (high grassland v.s. low grassland) to be eventually studied and experimentally analyzed near the York University Grasslands area. This is because as a group, members will be responsible to carry out the experiment involving a line transect and a sweep net. A sweep net collectively with a line transect will be utilized in order to sweep both insect(s) and spider(s) within areas of each of the two separate chosen spots of grassland. Walk transects (predetermined distance wherein all target organisms that are touching the tape/rope is sampled or where distance to the line is measured) in the grassland for a specified length of time (key variable to record as the longer one sweeps the likelihood of more being caught) and record total number of captured insects and spiders in combination with the total number of unique and recognizable insect taxonomic units (rtus) detected for each trial/replicate. Repeat approximately ten to twenty times (preferably fifteen trials conducted in both grassland types) throughout the given area being tested.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".