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

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

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

Notice bibliographique

RevueFigshare · 2016
Typedataset
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBotany and Plant Ecology Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAbundance (ecology)Plant diversityCover (algebra)Vegetation (pathology)Vegetation coverDiversity (politics)Plant coverEcologyPlant speciesPlant communityEnvironmental scienceGeographyBiologyEcological successionGrazingSpecies richnessEngineering

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: aucune
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,078

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,230
Écart entre enseignants0,163 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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