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Record W7070868572

Preliminary Maple Sap Data For Boxelder and Norway Maples

2023· article· en· W7070868572 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

VenueDigital Commons - USU (Utah State University) · 2023
Typearticle
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMapleSugarAceraceaeYield (engineering)Nutrient
DOInot available

Abstract

fetched live from OpenAlex

Maple sap is a popular agricultural product mainly produced in Quebec, Canada, and the Northeastern United States (U.S.).Almost the entire worldwide production of maple syrup exists in this area. Sugar maples (Acer saccharum) are the most popular tree to tap due to its higher sugar content. This species of maple doesn’t grow well in Utah’s soils, which might be a contributing factor to the lack of maple syrup production Utah. There exist about 211,714 boxelder (Acer negundo) trees that could be tapped in Utah. Estimates aren’t available for Norway maple (Acer platanoides) populations, but they exist in almost every park and street in Utah. Little to no information exists about the sap yield and quality of these maple species, and no information on how well they produce in Utah. The objectives of this research is to evaluate 1) the sap yield of the different maple species over the season, 2) the sap quality, 3) the effect soil nutrients has on sap nutrients and quality, 4) the effect tree circumference has on sap yield, and 5) the effect temperature has on sap yield.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.000
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 head0.038
GPT teacher head0.234
Teacher spread0.197 · 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