Bibliographic record
Abstract
An assessment of the capabilities of biotechnology core facilities requires access to current data on state-of-the-art technologies, personnel, space, services, financial issues, and the demand for such facilities. Data on these topics should be useful to researchers, facility personnel, administrators, and granting agencies.To obtain such data, the Association of Biomolecular Resource Facilities (ABRF) conducted a general survey on the operation and technical capabilities of core facilities. A total of 81 ABRF core laboratories voluntarily responded to the survey. Just over 60% of the respondents were from academic institutions, with the remaining located in research institutes, industry, and one U.S. government laboratory. Fifty laboratories provided financial data, with 47 of these operating on a nonprofit basis. Four laboratories were fully self-supporting from user fees.A typical facility had three full-time staff members and occupied approximately 1100 square feet (ft(2)). The most frequently offered services were N-terminal protein sequencing, protein fragmentation, peptide synthesis and purification, amino acid analysis, DNA synthesis, and DNA sequencing. One third of the facilities provided mass analysis by matrix-assisted laser desorption and ionization (MALDI) mass spectrometry, a recently introduced service that has been offered on an average for 3 years. Another relatively new service, bioinformatics support, is offered by about one third of the responding laboratories.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".