MétaCan
Menu
Back to cohort
Record W2967960094 · doi:10.13031/aim.201901312

The Canadian Integrated Northern Greenhouse: four-season testing and future opportunities

2019· article· en· W2967960094 on OpenAlexaboutno aff
David Leroux, Mark Lefsrud

Bibliographic record

Venue2019 Boston, Massachusetts July 7- July 10, 2019 · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouseUnit (ring theory)Biomass (ecology)Greenhouse gasEnvironmental scienceFood securityCanopyGeographyAgricultural scienceAgricultureAgricultural engineeringAgricultural economicsEngineeringHorticultureMathematicsBiologyEcologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

<b>Abstract.</b> Food security has become a prominent issue in northern Canada, and the high cost of transportation is a critical factor that contributes to the inaccessibility of fresh produce. Keeping in mind that many constraints, including environmental, cultural and economic barriers, cause food insecurity in northern Canada, local food production is one proposed solution to the northern food crisis. Initiated at McGill University by the Biomass Production Laboratory, the Canadian Integrative Northern Greenhouse (CING) unit is a completely integrative design that could allow northern Canadian communities to grow their own fresh and nutritious food, year-round. The CING unit is a hybrid between a northern greenhouse and a growth chamber, housed in a shipping container. It was designed to be adaptive, functioning as a typical solar greenhouse when solar light provides considerable heat and light, and as a closed growth chamber during the night and colder, darker winter conditions. Other components, such as a vertical hydroponic growing system, inter-canopy LED lighting, heating and ventilation, as well as a complete automation system, have all been designed specifically to fit the CING unit‘s requirements. The first working CING unit prototype is now functional and full-scale testing is now complete. The main objective of these tests was to compare the dry mass and plant health, as well as environmental and weather data of lettuce grown in the CING unit over 4 consecutive growing cycles to plants grown in a typical glass research greenhouse, tested lasted 3 to 4 weeks. In addition, we wished to demonstrate that even with less energy consumption, growing conditions in the CING unit were comparable to those found in a typical research greenhouse. The secondary experiment concerned the comparison of a biological nutrient solution and an inorganic nutrient solution, in both growth environments. The first cold condition growing 3 weeks test run (December 2018) was performed when temperatures were below freezing point (0 °C) outside the CING. Subsequent tests were completed in Spring and Summer 2018. In cold conditions, lettuce plants grew in the CING, but to a lesser extent than in the research greenhouse, on the average fresh and dry mass basis of the plants grown. In the research greenhouse, both nutrient solution treatments resulted in greater yields than in the CING, but the difference between treatments in the CING was less obvious. In the greenhouse, the inorganic nutrient solution resulted in a greater yield than the biological nutrient solution for every test. The greatest yield obtained in the CING was in March 2019, where the plants grown achieved 72% of the dry weight of the plants grown in the research greenhouse. Being the first prototype of its kind, the CING needs multiple improvements to be a fully functional unit, but efforts are being made to implement a unit in northern Canada, since different northern researchers have expressed interest in hosting such a unit. However, designing a unit that would fit the needs of a community must be done in full communication with future owners and operators of this food production unit. Building a pilot unit in a northern region is the next clear step for this project.

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 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)0.001
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.226
Teacher spread0.182 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue2019 Boston, Massachusetts July 7- July 10, 2019Same topicBerry genetics and cultivation researchFrench-language works237,207