Saint‐Gobain launches new product donation initiative.
Bibliographic record
Abstract
Construction materials manufacturer Saint-Gobain North America has launched a partnership with Good360, a leading coordinator of in-kind and product donations, to support the organization's disaster preparation and relief efforts throughout the United States and Canada. The partnership will enable greater investment of resources distributed in local communities recovering from the effects of natural disasters and embedding corporate social responsibility in the company's actions and decisions, Saint-Gobain said. The partnership includes a $60,000 grant from the Saint-Gobain North America Foundation, in-kind donations of light and sustainable building materials for rebuilding efforts, and volunteer hours to support preparation of supplies used for disaster relief efforts managed by Good360, the company said, noting that Good360's comprehensive approach to providing support in all phases of a disaster—from preparation to immediate response, to long-term recovery—aligns with the Saint-Gobain North America Foundation's aim to provide relief for individuals and communities impacted by disaster as they begin the long path to recovery. According to Good360, the funding from Saint-Gobain will strengthen Good360's ability to source and distribute building supply donations to get survivors back in their homes faster in the wake of a disaster event.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 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".