Bibliographic record
Abstract
Abstract Per Andersen was one of the leading neuroscientists of the second half of the twentieth century. He spent his entire career at the University of Oslo, apart from an exceptionally productive postdoctoral period with Sir John Eccles FRS at the Australian National University in Canberra between 1961 and 1963. As a PhD student he laid the foundation of field potential analysis, which allowed synaptic function in laminated cortical structures such as the hippocampus to be followed for long periods in intact animals, an essential technical prerequisite for the study of the synaptic basis of memory. In Canberra he identified the neurons responsible for feed-back inhibition in the hippocampus and feed-forward inhibition in the cerebellum, the first inhibitory neurons to be functionally identified in the mammalian brain. Per's Canberra achievements catapulted him to superstar status, and enabled him on his return to Oslo in 1963 to establish a laboratory that played a major role over the following decades in characterizing the functional properties of hippocampal synapses. From his laboratory emerged a string of significant advances, including the discovery of long-term potentiation, a form of synaptic plasticity widely believed to support learning and memory, and the introduction of the transverse hippocampal slice, which rapidly became the dominant preparation for investigating the properties of hippocampal neurons. Young researchers, many from the USA and Canada, flocked to his laboratory for postdoctoral training, adding to the steady stream of Norwegian doctoral students, two of whom went on to win the Nobel Prize. Per was also committed to the importance of communicating scientific advances to the general public, and contributed prolifically over the years to the Norwegian media, commentating on advances in neuroscience and their relevance to neurological disorders.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".