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Enregistrement W2461570514 · doi:10.6084/m9.figshare.1563632.v3

Observation of insect abundance and diversity through pan trap sampling methods in Danby Woodlot and Grassland, York University

2015· article· en· W2461570514 sur OpenAlexaboutno aff
Duong Bonnie

Notice bibliographique

RevueFigshare · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueRangeland and Wildlife Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGrasslandAbundance (ecology)Trap (plumbing)EcologyGeographyDiversity (politics)ForestrySampling (signal processing)AgroforestryEnvironmental scienceBiologyMeteorology

Résumé

récupéré en direct d'OpenAlex

This study was done in collaboration with 4 other team members consisting of Muhammad Akram, Daniel Germani, and Markian Plawiuk, and Nicole Gallagher. The purpose of this study was to collect a dataset of organisms gathered from using pan traps in order to familiarize group members with this method of sampling. Pan traps are particularly applicable towards the estimation of the abundance and diversity of flying insects, and are great at capturing pollinators. The experiment took place at the Danby Woodlot and Danby Grassland at York University Keele campus in Toronto, Ontario, on Tuesday September 29th, 2015. The Danby Grassland consisted of a large abundance of plant species no taller than knee height, with a clumped dispersion pattern, as patches of individuals crowding together were common. The Danby Woodlot consisted of an abundance of different species of trees of various heights and canopy coverage. Overall, there were high amounts of canopy coverage within the entire Woodlot, as low amounts of sunlight were able to pass through. This resulted in less precipitation present within the Woodlot compared to the amount of precipitation present in the Grassland. There were minor amounts of debris present within both areas at the time of study. The temperature was approximately 17 degrees Celsius, and it was rainy and gloomy with cloudy conditions, with continuous and constant wind travelling at approximately 23 km/h. The precipitation continued throughout the entire duration of experimentation, which started at approximately 3:19PM. Pan traps were completely set up by 3:37PM, data was collected at 4:20PM, and the experiment concluded by 4:34PM. Within the Grassland, nine pan traps were set up in a linear formation 2 metres apart each (18 metres on length total), alternating in colour: white, blue, yellow. The differences in colour of the traps were to account for the ability to attract different species of pollinators. Soapy water was filled into each trap approximately 1cm deep to account for the precipitation that was occurring to minimize the possibility of overflow, and to minimize the escape of insects as much as possible. The pan traps were set up at the edge of the Grassland closest to the Woodlot, where there was the least chance of disturbance to the traps by activity ocurring in the Grassland. This setup also allowed maximal sunlight exposure (although it was a rather gloomy day), and the pan traps were ensured to not be placed under heavy vegetative cover. Within the Woodlot, the same procedures of setup were repeated – however, the nine pan traps alternated in colour in a white, yellow, blue arrangement instead. The pan traps were placed close to the centre of the Woodlot in a linear formation. The pan traps were left undisturbed in both areas for about 1 hour. Upon data collection, the contents of the bowls were poured through a sieve for easier examination of the specimen collected, and for counting the frequency of individuals collected in each bowl. Further inspection was conducted by using metal prongs to pick up each organism to take a closer look at the species in order to assign an RTU to it. Overall, 18 pan traps were set up – 9 were located in the Grassland, while the other 9 were located in the Woodlot. Efficiency and collaborative efforts within the group was ensured by having a discussion between all group members in regards to experimental setup and design prior to the actual setup within the areas of study. While Bonnie Duong and Nicole Gallagher were the main coordinators for this particular data set, other group members worked efficiently by helping gathering materials needed, ensuring setup was done correctly and provided input, and aided in cleanup efforts after data collection was completed. Due to the constant precipitation during experimentation, it is likely to have affected the overall data collected. The rain likely played a role in lowering the number of organisms collected, as well as lowering the number of different RTU’s collected since more organisms were likely to be caught in the absence of rainy weather.

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)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,130
Score d'incertitude au seuil0,259

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,198
Tête enseignante GPT0,301
Écart entre enseignants0,103 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2015
Routes d'admission1
Résumé présentoui

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