YorkU.grassland.disturbed.open.space.oct19-2016
Bibliographic record
Abstract
The study was conducted on October 19, 2016 in two locations: Saywell grassland (representing grassland habitat) and Library lane (representing disturbed open space) of York University (Keele Campus), Toronto, ON by Pham Ha Phuong Do, Victor Suay Espi, Achal Amin and XinLu (Andrew) Fan. The temperature was 21 degrees Celsius, clear sky with cool breeze occasionally. There was a lot of sunlight as the survey was conducted in open field. For data set #1 (Herbaceous plants) - conducted by Andrew Fan, a quadrat is placed randomly every 2 meters (repeat 25 times in total) along the transect. Native and exotic plants are identified and counted. The total number of observed flower heads within quadrat is also recorded. For data set #2 (Woody plants) - conducted by Pham Ha Phuong Do, every 2 meters along the transect (repeat 25 times in total), any woody plant higher than 1.5 meters and within 0.5 meters on either side of the transect will be counted. At these points, canopy coverage is also estimated by dividing visual area into quadrats then sum the area covered. The same method is used to estimate vegetative ground cover as well as record the total number of flower. For data set #3 (Vertebrates & Invertebrates) - conducted by Achal Amin, data was collected for vertebrates by recording the total number of vertebrate individuals (including people who are not in lab group) in 50 meters radius from the beginning point of the transect. In another 15 minutes interval, the invertebrates data was collected by observing the amount of invertebrates present in a smaller area - 5 meters radius from the beginning point of the transect. For data set #4 (Invertebrates) - conducted by Victor Suay Espi, insects captured via 6 pan traps placed 3 meters apart from each other alongside a transect while alternating colours (blue, white and yellow) and 10 sweep nets next to the 50 meters transect. Each sweep transect is one replicate, and the number of invertebrates is recorded after each sweep. The number of invertebrates found in the traps is recorded at the end of the session. Key to variables:- abundance.native.plants: total number of individual plants recorded inside quadrat in data set #1 that are native to Ontario.- abundance.exotic.plants: total number of individual plants recorded inside quadrat in data set #1 that come from places other than Ontario.- total.number.flowers (quadrat): total number of flowers inside quadrat recorded in data set #1.- abundance.woody.plants: total number of individual woody plants higher than 1.5 meters within 0.5 meters on either side of transect recorded in data set #2.- canopy.cover: percentage of sky view (looking up) covered by canopy recorded in data set #2. - ground.cover: percentage of ground covered by vegetation recorded in data set #2.- total.flower.numbers (transect): total number of flower heads within 0.5 meters on either side of transect recorded in data set #2.- abundance.vertebrates: total number of individual vertebrates observed within a 50 meters radius from location recorded in data set #3.- vertebrate.richness: the number of different vertebrate species recorded in data set #3.- abundance.human: total number of people that do not belong to our lab recorded in data set #3.- abundance.invertebrates.pantraps: total number of individual invertebrates caught by pan traps recorded in data set #4.- abundance.invertebrates.sweeps: total number of individual invertebrates caught by sweep net recorded in data set #4.- abundance.invertebrates.observed: total number of individual invertebrates caught within a 5 meter radius during 15 minutes interval recorded in data set #3/4. Hypothesis:Data set #1: There're more exotic plants than native plants in the grassland because humans might've introduced exotic grass species that will grow well in campus condition. Data set #2: The woody plants in grassland are relatively small comparing to the woody plants in the forest, can't cover a big area. Data set #3: Population of vertebrates would be significantly higher than the population of invertebrates because there're a lot people and potentially their pets walking around campus. Data set #4: Pan traps will capture a higher number of invertebrates than the sweep net because netting captures mainly bigger individual invertebrates while pan traps can catch many small invertebrates. PredictionsData set #1: The number of exotic plants is significantly higher than the number of native plants. Data set #2: Canopy cover in grassland is very low. Data set #3: More vertebrates will be observed in the area than invertebrates. Data set #4: Sweep net will catch fewer invertebrates, while pan traps will catch many invertebrates.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.483 | 0.240 |
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; the direct Gemma label and the distilled Codex classifier 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".