Konkurranseevne for norsk oppdrettslaks: Kostnader og kostnadsdrivere i Norge og konkurrentland
Bibliographic record
Abstract
Denne rapporten viser og diskuterer utviklingen i kostnadene for produksjon av laks, samt drivkreftene bak utviklingen, både i Norge og i de viktigste konkurrentlandene. Kostnadene i Norge fortsetter å øke, men i mindre grad enn de siste årene. Kostnadene i konkurrentland øker også. Norske produsenter er blant de mest effektive, men Chile har nå kommet tilbake i posisjon som det mest effektive produsentland, mens Færøyene har fått svekket sin kostnadsposisjon. Drivkreftene bak denne økningen er de samme som i Norge, men med litt ulik styrke i de forskjellige land. Chile har hatt en betydelig bedring i de biologiske resultatene, noe som viser igjen i kostnadene. Færøyene har tapt noe terreng etter å ha fått større utfordringer med lus og sykdom. Skottland og Canada har de høyeste kostnadene, men mens økningen har vært moderat i Canada de siste årene, har den vært veldig stor i Skottland.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads 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".