Development of the Canadian agri-food lifecycle data centre with data format interoperability requirements
Bibliographic record
Abstract
The field of Life Cycle Assessment (LCA) models the resource flows and emissions characteristic of real-world industrial, agricultural, and economic activities through the use of Life Cycle Inventory (LCI) datasets. As the amount of data available to LCA practitioners through national and commercial database initiatives increases, there have been growing concerns within the LCA community regarding the interoperability of LCI data. Choice of data format and nomenclature poses problems for re-usability, as a dataset may not cleanly integrate into an LCA model due to differences in nomenclature, or a practitioner’s LCA software may simply not recognize the format type. This interoperability has been identified as one of the largest problems, along with data availability, in the LCA field. The focus of this research was the development of a new national Life Cycle Inventory database: The Canadian Agri-food Life Cycle Data Center (CALDC), which will serve as a central repository for Canadian agri-food data. During the course of the research, information was solicited from existing LCA database providers to inform development, and potential solutions for the interoperability issues were researched and implemented within the CALDC. The development included a searchable public database repository, as well as a web application that allows users to create, modify, and publish new LCI datasets, known as SimpLCIty. A set of recommendations were drafted for new LCI database initiatives, with the goal of increasing the interoperability between databases and datasets and increasing the availability of data. These recommendations were used in the development of the CALDC, and also present potential future avenues for expansion and development, such as the implementation of Application Programming Interfaces (APIs) or the re-distribution of datasets through third-party data providers and initiatives. The Canadian Agri-food Life Cycle Data Centre is now live, and is currently being used by researchers at both UBC and external stakeholder partners such as the Canadian Roundtable for Sustainable Beef (CRSB) to create and publish new publicly available agri-food data for LCA research.
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How this classification was reachedexpand
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 | 0.044 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".