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Record W2032945247 · doi:10.13031/2013.22254

Automated Fertilizer and Water Delivery System for Potted Plants

2006· article· en· W2032945247 on OpenAlexfundno aff
Trevor P Kondratowicz, T.G. Crowe

Bibliographic record

VenueApplied Engineering in Agriculture · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsNutrientFertilizerEnvironmental scienceDelivery systemVolumetric flow rateCommon emitterEngineeringChemistryAgronomyBiomedical engineeringElectrical engineeringBiologyPhysics

Abstract

fetched live from OpenAlex

A system capable of accurately administering controlled amounts of nutrients to potted plants was developed in support of experiments to study the effects of growing conditions on the reflectance of plants. This article outlines the components involved with such a nutrient delivery system and the tests performed to describe its performance. Eight solenoid valves, one gear pump, three manifolds, 48 emitters, tubing, electronics, storage tanks, and a data logger were used to construct an automated delivery system. The system was capable of delivering water and three different nutrients to 48 plants growing under three different nutrient regimes. In order to be able to describe the system's performance, three tests (a flush test, an emitter flow rate test, and a uniformity test) were completed. The results of the emitter flow rate test were an average of 71.5 mL/min, standard deviation of 2.7 mL/min, and coefficient of variance of 3.8%. The flush test indicated that 20 s was required to flush the system entirely of a 2000-ppm NaCl solution. The uniformity test indicated a salt solution was administered to a group of emitters within 2% of the expected quantity.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.146
Teacher spread0.143 · 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
Published2006
Admission routes1
Has abstractyes

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