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Record W4394200068 · doi:10.6084/m9.figshare.1590996

Investigation of the influence of tree size and tree condition on insect abundance of maple trees in Danby Woodlot, York University

2015· dataset· en· W4394200068 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2015
Typedataset
Languageen
FieldChemistry
TopicPlant-Derived Bioactive Compounds
Canadian institutionsnot available
Fundersnot available
KeywordsMapleTree (set theory)Abundance (ecology)ForestryGeographyEcologyBiologyMathematics

Abstract

fetched live from OpenAlex

This study was done in collaboration with 3 other team members consisting of Muhammad Akram, Daniel Germani, and Markian Plawiuk. The purpose of this study was to collect data in order to investigate the influence of tree size and tree condition of maple trees on the abundance of insects within close proximity to the tree. It was hypothesized that there would be a greater abundance of insects if trees were dead and/or large, compared to trees that were alive and/or small. It was predicted that the abundance of insects found in proximity of the trees in predicted in descending order would be: dead and large trees (greatest abundance of insects), dead and small trees, alive and large trees, alive and small trees (least abundance of insects). The experiment took place at the Danby Woodlot at York University Keele campus in Toronto, Ontario, Canada. The Danby Woodlot consisted of an abundance of different species of trees of various heights and canopy coverage. Overall, there were moderate amounts of canopy coverage within the entire Woodlot, as moderate amounts of sunlight were able to pass through. The Woodlot also had a moderate amount of leaves on the ground, as the study took place in the early Fall season. Data collection was completed on two separate days, each day 1 week apart from each other. The first day of study was Tuesday October 13th 2015, which was a cool and slightly windy, cloudy day with a temperature of 15°C and a wind current of approximately 10km/h. On this day, data collection began at 2:57PM and concluded at 5:05PM. The second day of study was Tuesday October 20th 2015, which was a cool and gloomy day with light drizzles and a temperature of 17°C and a wind current of approximately 11km/h. On this day, data collection began at 2:47PM and concluded at 4:53PM. Over the two days of data collection, 25 maple trees from each category were studied: alive and small, alive and large, dead and small, dead and large. To locate the next tree to investigate, the members of the group continually walked East further into the Woodlot after sampling the first maple tree found at the centre of the Woodlot. The size of the tree was determined by measuring the dbh using a measuring tape. The number of insects within a 1m2 quadrat placed 1 metre to the left/West of the tree of interest was counted by all four members of the group collaboratively (with each member responsible for 1 side of the quadrat) while using a digital stopwatch to ensure that the duration of counting was consistently 60 seconds for each tree under investigation. On the first day of data collection, 10 maple trees belonging to each of the four categories were studied, while 15 maple trees of each category were studied for the second day of data collection. In total, 100 maple trees were sampled. Due to the precipitation occurring on the second day of data collection, it was believed that the data collected may have been affected. This was due to how the abundance of insects was likely to be affected by the uncontrollable weather conditions which differed from the first day of data collection.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.604
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.227
Teacher spread0.184 · 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