Food Rescue at the Farm Through Gleaning
Bibliographic record
Abstract
According to the USDA, each year, we waste about 96 billion pounds of food in this country, including this year. At the same time we have about 40 million Americans who don’t get enough to eat. If you do the math, we are wasting about one and a quarter tons of food for every hungry man, woman, and child in this country. We are throwing away more than enough food than is needed to feed every hungry person in the U.S. We have already beaten hunger in America – we simply continue to allow it to exist. It doesn’t need to be that way; we let it be that way.\nDoesn’t it make sense to simply bridge the gap between all that excess food and the people that need it? There is a way to actually do that and it is having great success.\nThere are two main points of large volume food waste. One is right in the fields at harvest time where produce that isn’t yet ready to be picked or doesn’t meet tight specifications is simply left behind to rot. The other is in the produce packing and distribution centers where food that is harvested goes through a grade-out process before it is packaged for shipping to grocery stores. Billions of pounds of excess food is loaded into trucks and taken to landfills and dumped as waste, although it is perfectly good to eat. Food waste is the second leading cause of methane gas in our atmosphere.\nEach year the Society of St. Andrew coordinates with thousands of farmers and packing facilities, tens of thousands of volunteers and thousands of feeding vital programs in all 48 contiguous states to save and distribute 30-40 million pounds of this healthy but excess bounty. Society of St. Andrew is recognized as our nation’s premier gleaning organization and we save and distribute fresh produce in all 48 contiguous states. Using good stewardship practices for food, and all resources, we are able to feed the hungry in America at a cost of just 2¢ per serving with total overhead costs of just 3.2%.\nwww.endhunger.org
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.205 | 0.083 |
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".