Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future
Notice bibliographique
Résumé
<em>Abstract.—</em>It was not too long ago that our best available science advised that Pacific salmon abundance could be rebuilt to historic levels by adding more fish to the ocean. In Canada it was proposed that an enhancement program would not only benefit Pacific salmon, but it would also be repaid when the increased abundances were fished and taxed. The belief that there was unused carrying capacity in the ocean that could be filled with hatchery-reared fish persisted into the 1990s as indicated by plans to rebuild coho salmon, <em>Oncorhynchus kisutch</em>, stocks. It was in the 1990s that most researchers accepted that Pacific salmon production was related to trends in ocean carrying capacity. It was also accepted that regimes were real, resulting in persistent states in carrying capacity that shifted quickly to new states on a decadal scale. It was unsettling that climate trends could be directly related to Pacific salmon production because fisheries management science at the time did not include climate as a major factor that caused trends in production. The recognition that climate was a major factor regulating salmon productivity was also alarming since most scientists believed that humans were rapidly changing the climate. An additional concern was that hatchery-reared Pacific salmon were now common throughout the distribution of Pacific salmon and it was uncertain how the ability of Pacific salmon to adapt to climate variability had been compromised by the intermixing of hatchery and wild fish. The days of blaming everything on overfishing are gone. Providing the best available scientific advice now requires maneuvering through the uncharted waters of climate change with a science that has lost some of its steerage. The solution may be something we have known for years. Fisheries management science must improve forecasts. Model forecasting on a large scale may improve greatly as we discover the planetary forces that shift climate regimes and alter the trends in ocean carrying capacity for Pacific salmon. Model forecasting on a regional scale will also improve as the linkages between climate and marine survival are discovered. Fisheries scientists need to form teams that include biologists, oceanographers, climatologists, and perhaps physicists. Science organizations that find ways to establish, recognize, and reward these teams will probably provide the best management advice.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».