Bibliographic record
Abstract
I have spent most of my living and working life in the countryside, surrounded by open fields, woodlands and hills, and in close contact with the soil. I recently changed my job and moved to the University of Manchester, which is in the centre of one of the largest cities in England. Because of this move my contact with soil is much less; in fact, as I walk each morning to my office, there is hardly a handful of soil to be seen. But is this really true of the whole city? Concrete, asphalt, and bricks certainly seal much of the ground in Manchester, as in most cities and towns. But soil is in abundance: it lies beneath the many small gardens, flower beds, road and railway verges, parks, sports grounds, school playing fields, and allotments of the city. In fact, it has been estimated that almost a quarter of the land in English cities is covered by gardens, and in the United States, lawns cover three times as much area as does corn. As I write, I am on a train leaving central London from Waterloo Station, and despite the overwhelming dominance of concrete and bricks, I can see scattered around many small gardens, trees, flowerpots and window boxes, overgrown verges on the railway line, small parks and playing fields for children, football pitches, grassy plots and flower beds alongside roadways and pavements, and small green spaces with growing shrubs outside office blocks and apartments. The city is surprisingly green and beneath this green is soil. Throughout the world, more and more people are moving to cities: in 1800 only 2 per cent of the world’s population was urbanized, whereas now more than half of the global human population live in towns and cities, and this number grows by about 180,000 people every day. This expansion has been especially rapid in recent years.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".