Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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.
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; both teacher heads agree on what is shown here.
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".