What does Life‐Cycle Assessment of agricultural products need for more meaningful inclusion of biodiversity?
Bibliographic record
Abstract
Summary Decision‐makers increasingly use life‐cycle assessment (LCA) as a tool to measure the environmental sustainability of products. LCA is of particular importance in globalized agricultural supply chains, which have environmental effects in multiple and spatially dispersed locations. Incorporation of impacts on biodiversity that arise from agricultural production systems into environmental assessment methods is an emerging area of work in LCA, and current approaches have limitations, including the need for (i) improved assessment of impacts to biodiversity associated with agricultural production, (ii) inclusion of new biodiversity indicators (e.g. conservation value, functional diversity, ecosystem services) and (iii) inclusion of previously unaccounted modelling variables that go beyond land‐use impacts (e.g. climate change, water and soil quality). Synthesis and applications. Ecological models and understanding can contribute to address the limitations of current life‐cycle assessment (LCA) methods in agricultural production systems and to make them more ecologically relevant. This will be necessary to ensure that biodiversity is not neglected in decision‐making that relies on LCA.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".