Development of the Canadian agri-food lifecycle data centre with data format interoperability requirements
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it