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Record W4242062912 · doi:10.24908/iqurcp.7878

11. MEMS Seed Sorting Mechanism

2017· article· en· W4242062912 on OpenAlex
Marc Burnie

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.

venuePublished in a venue whose home country is Canada.
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

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
Fundersnot available
KeywordsSortingMicroelectromechanical systemsDielectrophoresisProcess engineeringComputer scienceSelection (genetic algorithm)Process (computing)NanotechnologyMechanical engineeringBiochemical engineeringElectronic engineeringMaterials scienceEngineeringMicrofluidicsArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

Seed sorting is an essential task in agriculture for maintaining the quality and purity of the yield. The current method for sorting seeds in the 50-200μm range is both tedious and inefficient. To increase productivity in the micro-scale an autonomous process using dielectrophoresis (DEP) selection techniques has been employed. By applying a controlled electric field, the seeds can be differentiated depending on their size and dielectric properties. Using existing Micro-Electro-Mechanical Systems (MEMS) technology, a preliminary working design was fabricated to prove the viability of the concept. Future iterations of the model will utilize counters and be fully automated with the ultimate objective of bringing the design to market.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.360
Teacher spread0.301 · 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