Research Magazine Summer 1995: Focus: Aquatic Science Expertise
Bibliographic record
Abstract
In this issue:Guelph's long tradition of aquatic research excellence; Welcome to an aquatic oasis; Top of the scale; A blood test for fish; A magnet for fish research; Moonstruck!; 100 tonnes every day; Toward a sustainable fishery; No place like home; Plotting a genetic roadmap for fish; Developing 'community friendly' aquaculture abroad; Scaling up; Char from afar; First in the field; Drawing a bead on fish disease; 911... for fish; Controlling viral disease; Working with 007; A voice for fish immune cells; On the brink of extinction; In search of cold-blooded polar giants; Managing human impact on the environment; Seals or scapegoats; Unlocking the copepod secret; Balms away; A new fish on the block; Sea quest; Blame it on nature; A crab's eye view; A catalogue of opportunistic creatures; Dealing with roundworm diseases; Musseling in; Shore enough; When plants hold their breath; Understanding gills' other function; An aquatic pitstop on the information superhighway; Probing fish history; Fishing for trouble; Red alert!; Promoting informed aquatic management; Environmental partnership spans the continent; Shore-friendly solutions; Environmental methodology; The Great Lakes other mussel menace; Aqua-invaders; Lights on, mussels hone; A slippery solution; A plethora of purple; Saving rainforest amphibians; Turning channels; Understanding the Fraser; In with the old, Lime-aid, Suntan lotion...for fish?, Send in the clones, A wonder of nature; What stimulates limb regenerations; Adaptation...or extinction; Singing a different tune; Hot tuna; Primitive fish shed light on kidney disease; Better heart health; Fast-food salmon; Head of its class; E.T., Ro and Eugenic; Swimming Flowers; A diet that's too rich...in vitamins!; First and foremost; Working towards cost-effective nutrition; Bringing nature to the laboratory; Understanding chemical exposure on food animals; Struggling with a naturally toxic effluent; The heavy-metal research question
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.374 | 0.260 |
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".