BC Tree Fruit System-of-Systems Information Architecture (Initial Design and Review)
Bibliographic record
Abstract
Precision agriculture (PA) technology used by growers can be an intimidating [1], complex [2] and at times frustrating [3] tool. In this paper we propose a high-level information architecture for the BC Tree Fruit industry to support 800 growers operating orchards that generate about $130 million in wholesale revenue and contribute $900 million in economic activity, directly employing about 1,500 persons per year <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The initial concept we propose involves: (1) design of a highlevel information system (IS) prototype to facilitate replacing the recording of records on paper and in-home databases with unified digital records; (2) design a digital database with appropriate access controls to facilitate appropriate data ownership; (3) a reporting system design consisting of a decision support system (DSS) or a system-of-systems (SoS), including business intelligence systems (BIs). By facilitating greater adoption of PA technologies growers can realize benefits including lowered costs [3] and the ability to adapt more readily to environmental conditions through water savings [4]. Additionally, by implementing this solution with industry partners in BC costs and knowledge can be shared.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Proposed information architecture for the BC tree fruit industry; agricultural business infrastructure, not research infrastructure.
The paper designs an agricultural information architecture for growers rather than studying research infrastructure.
Proposed information architecture for BC tree-fruit industry precision agriculture; grower industry systems, not research infrastructure as object of study.
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".