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Record W4394132984 · doi:10.6084/m9.figshare.3850788

Relationship between Grass Coverage on Plant Diversity, Total Vegetation Cover and Plant Abundance

2016· dataset· en· W4394132984 on OpenAlexaboutno aff
Helen Musulan, Justin Kim, Rita Sarwary, Justice Obiahuba

Bibliographic record

VenueFigshare · 2016
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicBotany and Plant Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbundance (ecology)Plant diversityCover (algebra)Vegetation (pathology)Vegetation coverDiversity (politics)Plant coverEcologyPlant speciesPlant communityEnvironmental scienceGeographyBiologyEcological successionGrazingSpecies richnessEngineering

Abstract

fetched live from OpenAlex

Meta-data: Travelled to a grassland at York University Keele campus to collect data (43.776131, -79.496162). This data was collected within a 1m x 1m quadrat. Data pertaining to total number of plant species was collected with cluster sampling low, medium(rep1-12) and high plants(13-25) by counting them individually and then adding the number of grass plants within ¼ of the quadrat times 4. The number of different species was counted and distinguished by comparing plants side by side. The most common grass species was considered a reference to distinguish species from the sedge and reed family. The total vegetation cover was visually approximated by percent; percentage of all plants covering the ground within the quadrat. The total grass cover was visually approximated by percent; percentage of only the grass species covering the ground within the quadrat. In considering total vegetation and grass coverage, we were considering both live and dead plant matter. In total an area of 25m2 was sampled starting from quadrats with high plants the same species from rep1-rep12 going to medium to low plants from rep13-rep25. The only selection factor was height but the species was variable. High plants are considered to be over 100cm and medium between 100- 20cm and low plants below 20cm. Attributes 1) Total number of Plants (within a quadrat): Numerical 2) Total Number of Different Plant Species (within a quadrat): Numerical 3) Total Vegetation cover by % (within a quadrat): Numerical 4) Total Grass cover by % (within a quadrat): Numerical Location: Data was collected at York University Keele campus at a grassland with few deciduous trees dotting the grassland. This grassland, on one side was bordering a dense forest with many deciduous and coniferous trees. Location with GPS coordinates was 43.776131, -79.496162. Outside this area were well-circulated roads. There were no sources of water nearby, neither waterfowl. Entire grassland was densely covered with grass both dead and alive. The weather was mostly cloudy with temperatures reaching 30C and humidity reaching 40%. Although the humidity was high and dark clouds were forming; it did not rain much (max 2mm) in the area. This is reflective of the entire summer in Toronto (2016); intense heat and very little precipitation. (1) 1: http://www.citynews.ca/2016/08/10/heat-wave-set-to-blanket-the-gta/. Method: Data was collected within a 1m x 1m quadrat by visual observation and cluster sampling. This quadrat was moved from area to area 25 times; moving from grassy areas with tall plants (rep1-12) to grassy areas low and medium height plants (rep13-25). High plants are considered to be over 100cm and medium between 100- 20cm and low plants below 20cm. We counted the number of non-grass plants first individually then split the quadrat into 4 and counted the number of grass plants in the ¼ section. We multiplied it by 4 and added it to the number of non-grass plants. The number of species was counted by visual discrimination and comparison to other plants species within the quadrat. Vegetation coverage was roughly determined by eying the percent of the quadrat’s ground was covered by the total vegetation. Grass coverage was roughly determined by eying the percent of the quadrat’s ground was covered by the only the grass species. All data was recorded first on to a notebook. Key to variables: Total number of plants observed within the quadrat was measured by first counting the non-grass species and then counting the number of grass plants within ¼ of a quadrat and then multiplying it by 4. The total number of different species was counted and species were distinguished by side by side comparison. The total vegetation cover was roughly measured by eye; how much of the vegetation covers the ground within the quadrat by percent. The total grass cover was roughly measured by eye; how much of only the grass covers the ground within the quadrat by percent. Hypothesis: The higher the coverage of grass can lead to lower diversity due to other plants being crowded out in nutrients, water and other resources. Since grass’ roots are so strongly attached to the soil, even after death, it gives little room for larger or different species of plants to push their roots. This is why there are very few large trees/plants in a grassland. Predictions: 1) In areas with a lower grass coverage but higher total vegetation coverage, we can expect a higher number of different species and lower number of total plants because of more soil room and nutrients to sustain larger and different plants. There are fewer plants because of the inter-species competition for soil room and the number of grass plants were the bulk of the total plants. 2) In areas with higher grass coverage and lower vegetation coverage, we can expect a lower number of different species and higher number of total plants because the grass species is able to spread very densely, dominating the area by outcompeting and out crowding neighboring plants. 3) In areas with a large number of individuals and a couple of different species, we can expect there to be a grass coverage of roughly half of the total vegetation coverage. In this case, the non-grass plants have been long established and not strongly affected by the roots of the grass plants.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.067
GPT teacher head0.230
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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