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Record W7027855164

Design and Assessment of a Geothermal Greenhouse in Northern Manitoba

2021· report· en· W7027855164 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2021
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeothermal gradientGreenhouseGeothermal heatingSustainabilityGeothermal energyGreenhouse gasElectricitySchedule
DOInot available

Abstract

fetched live from OpenAlex

This report outlines a feasibility study for a geothermal greenhouse for northern Manitoba communities. Currently, there is a lack of access to low-cost high-quality food in northern Manitoba communities thus leading to health issues for residents of these communities. The goal of this project was to provide a sustainable solution to this lack of healthy food by designing a greenhouse using a geothermal system as its primary heating source. A cost analysis, bill of materials, installation schedule and heating simulations using COM SOL will be provided in this report. Shamattawa First nations was determined to be a strong candidate for a geothermal greenhouse. Due to its location, the food cost in Shamattawa First nation are nearly 4 times higher on average when compared to Winnipeg. There is also no access to grid and all the main source of electricity in Shamattawa First Nations comes from a local diesel generating station with cost of $0.60 per kw/h. This combined with an issue with obesity and diabetes in the community made Shamattawa First Nation a prime candidate for the geothermal greenhouse. The greenhouse will be 500 m2 and will be 10x50m. The greenhouse will have 40% of the area allocated to a community area and the 60% allocated for growing. This was determined through an analysis of the required area for growing as well as determining efficient dimensions to reduce the heat loss to the surrounding areas. The greenhouse will have a full glass south facing wall at an angle of 70° and a retractable thermal blanket to maximize the solar heat gain and reduce heat loss at night. A steel frame with an OC of 6250mm combined with insulated metals panels for the siding and roof. The combination of these dimension and materials result in a well-insulated structure resulted in a net positive heat gain for all months of the year except December and January. The total energy requirement for the greenhouse was estimated to be 529 kWh per day. To achieve this necessary energy load, a 102 Hanwha Solar Canada HSC-250-60P solar panels and 10 Anorra 48 wind turbines were sourced. Three TROES 50kW,220kWh Indoor Cabinets were source to store energy for instances where there is not energy being harvested by the solar and wind. The greenhouse will utilize a horizontal closed loop geothermal system as its primary source of heating. The geothermal system will have 2700m of piping at a depth of 20ft and 25ft to meet the required heating loads and preventing the pipes from freezing. The use of Two WaterFurnace 7 Series 700A11 Heat Pumps to achieve a max heat pump capacity of 48000 BTU/hr. The total cost the greenhouse was estimated to be $1854210.00 CAD without taxes. It was determined that utilizing a geothermal system to heat a greenhouse in northern Manitoba is feasible. However, to determine if a geothermal greenhouse is a solution to lack of healthy food, community input will be required as well as a more detailed design and more in-depth cost analysis to determine if the greenhouse is economically feasible.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.217
Teacher spread0.186 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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