Bibliographic record
Abstract
Ivan Valiela and Paulina Martinetto correctly point out the increasing volume of academic literature published yearly and the challenges involved in keeping up to date (BioScience 55: 688–692). It is, however, disturbing to learn only an average of 36 percent of known publications were retrieved from online bibliographic databases. As science librarians, we feel their strategy and findings warrant a response since our knowledge of database search and retrieval may explain their results. First, ASFA and Biological Sciences provide good coverage of aquatic sciences; however, the choice of databases in the group column in table 1 is problematic since it was based upon what was available as opposed to their subject coverage. Better results may have been achieved if subject-relevant databases had been included such as BIOSIS Previews, Wildlife & Fisheries Worldwide, or Selected Water Resources Abstracts, instead of GeoRef, MEDLINE, PsycINFO, and TOXLINE. Second, the variability within databases occurs for several reasons. Databases employ indexing practices which may involve core indexing (full content) or selective indexing (less than 50 percent of the content) of journals. Valiela and Martinetto did not state whether they had verified the level of indexing and coverage of journals by ASFA or Biological Sciences, or if they had verified whether journal titles of unfound publications were indexed at all by the databases. Moreover, the lack of availability of publications prior to 1970 would be expected, considering that ASFA and Biological Sciences were first published in 1971 and 1982, respectively. Given the extensive date range of publications used in the study, the authors could have made the study format independent (i.e., print or electronic) for the publications dating back to the 1940s. This could include the print indexes Biological Abstracts or Zoological Record, as their coverage began in 1926 and 1864, respectively.
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 | 0.132 | 0.489 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.053 | 0.067 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.023 |
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".