Bibliographic record
Abstract
Regional realignments ith the rise in the popularity in birding in recent years and the acceleration of communication through the Internet, reports--as well as documented records--of notable birds have increased at a pace perhaps never before seen on this continent.This pace has occasioned some growing pains for a journal that has lived through most of its 85 years at a very different and gentler pace, and the structure of future issues of this journal will represent an attempt to adapt to the new high-speed world of birding.In the past, to be sure, regional editors received reams of material, at least in heavily birded or heavily populated regions.We recall tales of editors sitting down to distill the contents of letters and dossiers stacked two feet high for the fall season; the composition of the regional report could take weeks, with drafts passed back and forth by mail, carefully typed, edited, and retyped, and then typeset through the Audubon offices.The amount of work in producing a journal back then seems Herculean to an editor accustomed to receiving and sending all material in electronic form--no paper, no postage, no red pen.We have it easy today, by comparison.But at least, in the past, the photocopied field notes and summaries that reached regional editors usually carried some semblance of details about the less-usual birds reported; regional editors developed a longterm epistolary, if not a direct personal, relationship with contributors over the years; and contributors often felt compelled, without being asked, to supply a photograph or two to support their documentation.Rumors of "interesting" birds rarely made it into print.Regional editors in the current era face a different set of practices.The Internet is alive with tantalizing bird sightings, many of them doubtlessly correct, many of them clearly wishful thinking, few of them documented with photographs or by any other means that would meet even minimal standards for inclusion in regional reports.To chase down each report in a large or heavily-birded region is the equivalent of taking the plunge into Lewis Carroll's looking glass--what seemed substantive recedes or vanishes, while the fantastic looms large.The technology that would seem at first to ease an editor's work can in fact multiply his or her tasks beyond accomplishing, or at least frustrate even the most patient among us.There can be no doubt that thousands of tales speak against this scenario, marvelous triumphs in which a dever birder finds a bird of interest, photographs it, posts the œmding and the photograph, and provides nearly instantaneous documentation and bird-finding information to the public.It is refreshing that such tales are increasingly common.And refreshing, too, that birds of uncertain identity are photographed and become the stuff of international conversations that extend for weeks in the public domain (and, no, not just about gulls!).In addition to the billions of bird bits and bytes on the Internet, the flowering of states' and provinces' records committees means still more correspondence that requires a regional editor's attention; in regions that contain parts of (as many as) eleven states, such correspondence is ever more taxing and time-consuming.With most states east of 86 ø W now being partitioned between two reporting regions, we have reached the breaking point for some editors in terms of their ability to compose a balanced report in a timely, nuanced, and thoughtful fashion.This is not a matter of talent but one of time and administrative difficulty: the larg-�
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.492 | 0.484 |
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".