Bibliographic record
Abstract
During the past year, members of the Institute have actively engaged in furthering its mandate of promoting science in Nova Scotia.This has been achieved in six ways, by:• publishing the peer-reviewed Proceedings of the Nova Scotian Institute of Science, • presenting a program of monthly public lectures by expert Nova Scotian scientists, • providing financial support for Regional Science Fairs, • running the annual scientific writing competition, • running a mentorship program for university science students, and • developing and actively maintaining the Institutional web site.For the past several years, the Proceedings have been published under the very able editorship of Dr James E. Stewart who has maintained a high standard.He has been assisted by a dedicated Editorial Board.Jim has informed Council that he wishes to retire in the near future and Council has initiated a process to ensure continuity in this important institutional activity.Council is also aware of changes in the publishing world as the internet becomes more pervasive.The Institute is indebted to Dr Stewart and to members of the Editorial Board for their services.The Institute sponsored seven public lectures during the past year, all but one of which were held at the Museum of Natural History, which has for some time helped to support Institutional activities.Excellent lectures were given by G. Marangoni (applied chemistry, muds for oil drilling), A. Speers (food science, brewing beer), M. Leonard (biology, communications between birds), S. Murray and A. Guha (pathology, detection and treatment of a virulent form of cervical cancer), C. Watters (computer science, developing improved human interfaces for hand-held computers), R. Côté (environmental studies, integrating industrial and natural ecosystems), D. King (oceanography, developing and marketing biotelemetry systems).The January lecture was held at the University of King's College to which the Institute is indebted for the provision of space.Attendance at some lectures was disappointingly low.Council has considered the problem, which most likely stemmed from using titles and abstracts supplied directly by the speakers.The seminar coordinator will attempt to develop more publicly attractive titles and abstracts for the next year and Council has run a survey of NSIS members to help determine if seminars are meeting their needs.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.322 | 0.283 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".