Bibliographic record
Abstract
In my last editorial [1] in early 2020, I highlighted our past achievements and looked forward to seeing where the new year would take us, not expecting the year to turn out as it did.With the pandemic affecting all corners of the world, it would be remiss not to acknowledge COVID-19 and the consequences still felt now at the time of writing.I want to recognize, in particular, how researchers have had to adapt to unpredictable workloads while teaching, learning, writing, peer-reviewing and living under a hugely difficult situation for the last year and a half.Your efforts have not gone unnoted and Biology Letters will continue to support you where we can.Biology Letters continues to be a fast, high-quality journal, publishing short research articles, reviews and opinion pieces across the biological sciences.July marked the annual release of the Journal Citation Reports Impact Factors (IFs), calculated by dividing the number of citations from the last full calendar year divided by the number of source items published in that journal during the previous 2 years.And the excellent news here is that the Biology Letters IF has reached a record for this journal, equalling a value of 3.7, and a 5-year IF of 4.2.This places us in the top third of biology, evolution and ecology journals and is a testament to the high-quality science we publish.The diverse research articles Biology Letters publishes attract strong media attention (figure 1).In March 2021, Hal Whitehead and colleagues from Dalhousie University analysed digitized logbooks of American whalers in the North Pacific and discovered a 58% decline in whalers succeeding in harpooning sighted whales within a few years of exploiting a hunting region (https://royalsociety publishing.org/doi/10.1098/rsbl.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".