Integrating climate change, food security, and innovative agriculture in Newfoundland and Labrador (NL): A Water-Energy-Food (WEF) nexus approach
Bibliographic record
Abstract
This study examines the intersection of climate change, agricultural innovation, and food security in Newfoundland and Labrador (NL), a province characterized by a short growing season, poor and acidic soils, and a small agriculture sector highly vulnerable to climate change. Despite being one of Canada’s most food-insecure provinces, there is a significant lack of comprehensive studies on the Water-Energy-Food-Climate Change (WEF-CC) nexus and agricultural innovation in NL. The study aimed to (1) inventory innovative agricultural practices that promote food security and climate resilience, (2) identify key stakeholders in agricultural innovation, (3) explore factors influencing innovation in the province, and (4) assess the use of by-products in agriculture. Data were collected through semi-structured interviews and analyzed using NVivo content analysis. The findings revealed two primary types of relevant agricultural innovation in NL: practice-based and technology-based. Six key stakeholders in agricultural innovation were identified. However, the lack of an independent third-party innovation enabler or connector was perceived as a barrier to progress. To address this gap, the study proposes the establishment of the Newfoundland and Labrador Agricultural Innovation Centre (NLAIC), a collaborative body designed to support agricultural innovation. Additionally, opportunities for utilizing agricultural and industrial by-products, including plant-based and animal-based innovations, were identified as emerging in the province. Tackling innovation barriers and promoting nexus thinking and collaboration among stakeholders and sectors could enhance climate resilience and food security in NL.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".