Lab 2, Field Dataset 1: Utilizing Quadrats to Examine the Abundance and Diversity of Plant Species Within the Danby Woods Grasslands.
Bibliographic record
Abstract
Meta Data Attributes Description Quadrat Number (n): A square, metal quadrat (1m x 1m) was placed randomly in the grassland 25 times. The quadrat placed was divided into 4 subunits (0.5m x 0.5m). Only the bottom right unit of space was examined and displayed in this data set. Each unit of space observed is affiliated with a number (n). Total Abundance of Plants The amount of every plant present in the specific subunit of the quadrat (including grass and other species of plants). The amount of grass in the specific subunit of the quadrat was estimated by counting the number of clustered grass sprouting from the same root and location. The amount of individual plants of a different species (not grass) was counted one by one by the experimentors. Total Number of Different Plant Species: The amount of different plant species observed by the experimentors within the specific quadrat. Plants were determined to be of a different species based on observable morphological differences.Total Cover of Vegetation(%): The percentage of the specific subunit quadrat covered by any vegetation (including grass and other plant species). The percentage was an estimation made by the experimentors through close examination of the quadrat. Total Cover of Grasses(%): The percentage of the specific subunit quadrat covered by only grass. The percentage was an estimations made by the experimentors through close examination of the quadrant.Methods: To record this dataset, quadrats were placed randomly in the grassland, considering that there was a consistent range of environmental diversity. The quadrat used was a metal squared frame that measured the area of a square meter (1m x 1m). Random sampling was used; 1st quadrat (n=1), 2nd quadrat (n=2), and so on until n reached 25. This format of sampling was used to avoid and biases in the results. Each quadrat was placed in a random location within the designated grassland area mentioned by the TA. The quadrat was divided into subunits of 4 (0.5m x 0.5m) and the bottom right corner was used everytime to examine its' contents for the abundance, number of species, total cover of vegetation, and total cover of grass. Also the data collected is numerical. Study Site: This dataset was collected in the grassland near Danby Woods at York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada. This study was conducted on Thursday September 22nd, 2016 from about 2:45-3:30pm. The weather was mostly sunny, but for a short period of time (10 mins) there was a drizzle.Hypothesis: If the presence of grass and other species of plants are greater in shadier area compared to those exposed to the sun, then seed germination in a sunny habitat will be limited by the lack of moisture present in comparison to shadier habitats.Prediction:1. Little range of plant growth and dispersion in sunny parts of the grassland.2. The plants existing in the sunny patches of the grassland would mostly consist of dried out plants.3. Shadier areas will consist of more species heterogeneity and a widely dispersed plant growth.Group Members: Keerthana, Matthew, Abesan, and Andrew
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.022 |
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".