Greenhouse Gas (GHG) Scope 3 Inventory and Corporate Climate Strategy for Ocean Spray Cranberries Inc
Bibliographic record
Abstract
According to the Greenhouse Gas Protocol, the Corporate Value Chain (Scope 3) Accounting and Reporting standard, Scope 3 greenhouse gas (GHG) emissions constitute emissions that result from indirect activities or assets not owned by an organization. Of all the GHG emissions generated by the food and beverage industry, as much as 90% are Scope 3, often stemming from complex agricultural, manufacturing, and distribution supply chains out of the direct control of large brands (Greenhouse Gas Protocol, 2015). In an effort to address and mitigate climate change for their farmer-owned cooperative, Ocean Spray Cranberries (OSC) sought help to conduct a GHG inventory of Scope 3 emissions and identify Scope 3 reduction targets. The University of Michigan Master’s Project team was tasked with conducting a GHG inventory of Scope 3 emissions to assess the baseline of OSC indirect emissions. A value chain map was created in conjunction with OSC’s sustainability team. A specialized Scope 3 calculator relevant to OSC’S Scope 3 categories and updated emissions factors was developed as a tool to assess indirect emissions. In addition, the team helped calculate upstream transportation and distribution emissions reductions associated with shifts to regional production in Canada and Australia. This project provided tools to help inform OSC’s corporate climate strategy to mitigate the effects of and adapt to climate change. Students received better insight into the challenges associated with accounting for and reducing indirect Scope 3 emissions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".