Setophaga ruticilla
Bibliographic record
Abstract
Setophaga ruticilla (MGT): breeds in Alaska, Canada and eastern USA and migrates to Central America, Caribbean and northwestern South America, as well as to southern USA in small numbers (Curson, 2016a). In Brazil, it occurs only in the Amazon: in RR with records between September and April(Stotz et al., 1992; Sick,1997; WikiAves,2016), and in AM with records in January (WikiAves, 2016), April and November (Stotz et al., 1992) and October (MPEG 43351). This species departs from breeding areas in North America between July and September and reaches South America in October, returning from late March on and arriving at the breeding site in April-May (Curson, 2016a). It seems to exhibit higher fidelity to wintering areas in the Neotropics than to breeding areas in the North Temperate Zone, probably because individuals remain in the wintering area during their first year of life (Holmes & Sherry, 1992). Populations from the west of the wintering area originate from the northwest of the breeding areas and populations from the east of the wintering area originate from the east and south of the breeding areas. In general, this species reduces its migration distance by flying mainly along a north-south axis between its breeding and wintering sites (Norris et al., 2006).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".