Bibliographic record
Abstract
The ‘Three Sisters’ is an agricultural system in which corn, squash and beans are grown together. This type of system is very old and continues to be used in some communities and family gardens. From 2015 to 2018, Agriculture and Agri-Food Canada, which is a Department of the Federal Government, in collaboration with the Agricultural Society for Indigenous Food Products, implemented the Three Sisters project. Its main purpose was to study characteristics of varieties of corn, squash and beans and the products derived from them in order to develop added value for indigenous stakeholders, while also studying health benefits. Research activities included studies of traditional knowledge, e.g. on ancestral lineages of the Three Sister crops and their respective seed keepers, combined with studies relating to production, processing and use. Existing instruments identified in Canada were used in the project to select good practices. The project looked for principles, rules and mechanisms that enable Indigenous people to control the circulation of their resources and knowledge at each step of the research project (access, utilization and valorization), and also resulted in new knowledge on health and nutritional benefits and possible ways to protect and preserve genetic material of ancestral crop genetic resources.
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.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.024 |
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".