DEVELOPING A CONCEPTUAL MODEL TO EVALUATE SHELF-LIFE EXTENSION AS A RESILLIENT STRATEGY IN HUMAN MILK BANKS
Bibliographic record
Abstract
The COVID-19 pandemic and higher levels of uncertainty due to climate change have emphasised the need for resilient and secure supply chains. This is also true for Human Milk Banks (HMBs), which are responsible for providing screened and safe Donor Human Milk (DHM) to neonatal hospital infants. Given the perishable nature of human milk, current UK operations are constrained by practices yielding a maximum shelf-life of six months. For improved resiliency and improved adverse event preparedness we present a conceptual model of HMB operations, which is an augmented version of one previously communicated, incorporating an additional process pathway yielding DHM with an extended shelf-life of 18 months. To address the associated operational constraints, our new conceptual model provides a framework for exploring the potential benefits of extended shelf-life on supply chain performance, especially related to inventory management and an enhanced ability for human milk banks to face future disruptions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".