Bibliographic record
Abstract
“Conceived was I by sea and sky/Their elements are fused in me/Of brigand birds that float and fly/I am the freest of the free” (Robert Service, Grey Gull) With these lines, poet Robert Service (1874–1958) appears to describe the gull as freedom personified. And it's all too easy to agree. In our mind's eye, his words conjure up images of a soaring, confident spirit with the gift of being able to take from life whatever it pleases without consequence or cost. But beware: despite the poem concluding with the author's declaration that he might happily choose the life of his subject, the bird in his poem is actually making a rather self-important after-dinner speech to some chickens who, having seen him by hunger goaded and by cold, allow him to share their feed. For all his apparent liberty, Service's gull is free to go only where his needs demand. This understanding that freedom is in some way always fettered might help explain a phenomenon that has been gathering pace over recent years: the nesting of gulls in cities. Most of us probably think of gulls as seabirds, wheeling around cliffs, raucously gallawking in harbors, or racing along the seafronts of vacation towns. However, in many coastal cities you are just as likely to see a gull swoop between buildings to snatch an abandoned sandwich as to see one pick over seaweed for crabs. And for decades now, gulls of different species have forayed miles inland to feast at our landfills, while blackheaded gulls (Larus ridibundus), like those that roost on the King Charles Bridge in Prague, have long been doing their best to shrug off the name “sea” gull by living and even nesting hundreds of miles from the ocean. But, by breeding on city rooftops, some gulls are taking a giant leap toward abandoning the life marine. Meet the new neighbor. For some 60 years, this phenomenon has been reported from Canada, the US, Australia, and Europe. At first, instances were no more than anecdotal, with just a few pairs of birds taking up rooftop residences. But these pioneer populations grew, and nowhere more dramatically than in the UK. Some experts now number the country's roof-nesting gulls at over 100 000 pairs – a figure currently growing at an annual rate of 13%. Herring gulls (Larus argentatus), which prefer to nest on houses, and lesser black-backed gulls (Larus fuscus), which find industrial roofing more to their taste, are by far the most common, but great black-backed gulls (Larus marinus), common gulls (Larus canus), black-legged kittiwakes (Rissa tridactyla), and black-headed gulls are all in on the action. Even towns like Hereford, miles from the sea, have large nesting populations. Unfortunately, nesting gulls cause serious damage to roofing materials, their excrement corrodes and dirties buildings, some birds can be aggressive toward people, and lots of them are loud! In fact, they are such bad neighbors that many UK cities have begun gull control programs. Certainly, the great adaptability of gulls made this invasion possible. Able to feed on scraps thrown from fishing boats, to follow plowing tractors for worms, and to gobble up garbage from trash cans, they have proven themselves well able to make use of human-provided opportunities. So perhaps it was only a matter of time before they figured out that well-lit cities could provide them with 24/7 scavenging, and that rooftops are rather like cliffs, but unoccupied and without the spray. However, the fact that they could colonize our towns may not be the reason they did, for while British urban gull numbers are rapidly growing, their overall numbers are falling fast. According to the Royal Society for the Protection of Birds, all the UK's gull species have been designated of conservation concern. Indeed, the herring gull's decline is so serious (over 50% in the 30 years prior to the year 2000) that it has been placed on the IUCN Red List. As John Calladine, Senior Research Ecologist with the British Trust for Ornithology, points out, this might reflect a return to the situation before fishing discards (now rarer because of downsized fishing fleets) and other anthropogenic wastes (perhaps now better managed) led to inflated gull numbers. But it could mean that marine pollution and falling fish stocks have so altered coastal ecology that large, sea-going gull colonies can no longer be supported. Birds raising chicks in towns may be doing very well, but they may be pursuing this option because the sea no longer offers them what they need. Thus, rather than exercising any boundless freedom, the gulls taking over British rooftops may be doing so for the same reason that Service's bird fed with the chickens: to survive. Soaring brigands they may be, but tied to rooftops to be free?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".