Any change in the Methodology of field studies on bird Migration? A comparison of methods used in 1994-2003 and a Quarter Century earlier
Bibliographic record
Abstract
Any change in the Methodology of field studies on bird Migration? A comparison of methods used in 1994-2003 and a Quarter Century earlier The holistic approach to the study of bird migration observed in the past decades and the huge advancement in technology should be seen in the numbers and types of methods used in field studies for this phenomenon. To check this assumption, we compared field methods used in the studies on bird migration published in international journals in 1994-2003 ( N = 570 papers) and in 1967-1976 ( N = 394 papers). We noted an increase in the mean number of methods per a single paper (from 1.49 in the former of these decades to 1.98 in the latter) and a change in the frequency of each method. In recent years, methods such as satellite telemetry, DNA or isotope proportions analyses have been developed. An increase in the mean number of methods as well as changes of the most frequently used methods were more apparent in journals indexed on the ISI Master Journal List in 2003 than in other current journals, where the methods were often found to be similar to those applied a quarter century earlier, which surprised us.
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 | 0.160 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".