Bibliographic record
Abstract
Metadata Census 1 (Emma): abundance.native.plants refers to the total number of individual native (to Ontario) plants found in the quadrat. Rep represents the replicates which are each time we did the procedure. Abundance.exotic.plants refers to the number of non- native (to Ontario) plants found in the quadrat every replication. Total.number.flowers (quadrat) refers to the total number of flower heads found every replication in the quadrat. The quadrat sampling was done a 50 meter transect and the quadrant was randomly placed every 2 m and alternated sides. This data set is categorical data. Census 3 replicate 17 (Bukunmi) : Abundance.vertebrates refers to the total number of vertebrates that were counted along a 50 meter transect in a 15-minute interval. Vertebrates.species referes to the number of different species that were recorded along the transect. Abundance.humans referes to the total number of people that did not partake in the ecology lab but were counted and recorded in the 15 minutes. Abundance.invertebrates: referes to the total number of individual invertabrates that were counted along 5 meters of the 50 in 15 minutes. This categorical data was collected using point surveys. Census 3 replicates 1 -16 (Rania): abundance.invertebrates.pantraps refers to the total number of invertebrates found in each pan trap. The categorical data set was collected using pan traps that were placed 3 meters apart in a straight line. The pan traps were 1 of 3 colours; blue, white, yellow. Abundance.invertabrates.sweeps referes to the total number of invertebrates found at the end of each sweep. In total 10 sweeps were performed along a 50 meter transect. This categorical data was collected using sweep nets and each sweep took about 40 seconds. Census 2 (Angel): Using two transects to create a 50-meter line across the grassland. Every two meters, she collected data on how many trees (more than 1.5 meters in height) were present within 0.5 meters of the transect. Se also estimated the canopy coverage of the tress present by looking up and estimating how much of the sky was covered by branches. Vegetation coverage was also estimated by looking down and noting how much live vegetation was present with 0.5 meters of the transect at each point. Within the same area, the number of flowers present was also counted. Description: The purpose of this data set is to compare different campus environments. This data collection took place on October 3rd 2016 at the grassland collection took place near Stong pond on York University campus from 2:45 to 3:40pm on a cloudy day warm day. The disturbed area collection took place just off of Library Lane and at around 3:40 – 4:40pm The ground was also damp. The equipment that was used was; sweep nets, transects quadrats and pan traps. Most of the collections used a 50 meter transect as a guideline and the collections were done along it. Hypothesis That the grasslands will have for species and more species abundance than the disturbed area. Predictions 1. - The number of exotic species would be higher in the grassland for census 1 2. - More species in the grass land due since the disturbed area is disturbed and bothered and very scarce coverage. 3. - Also more variety in grassland due to the lack of speciation in the disturbed area.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.735 | 0.536 |
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".