IOC Workshop on Ocean Colour Data Requirements and Utilization, Sidney, Canada 21-22 September 1995.
Notice bibliographique
Résumé
Ocean colour data is essential for monitoring and fostering our understanding of \nimportant ocean biological processes. Adequate data pertaining to ocean biological processes is extremely difficult to obtain due to the vast area of the ocean (over 70 % of the earth’s area) and to the logistical difficulties of shipboard sampling. Satellite views of ocean colour are our only chance for gaining an overall view of the state of ocean biology at any given time. Ocean colour data is also the most practical way to develop the time-series data that will allow us to separate natural variability in ocean biological processes from secular changes. \nOcean colour data will allow us to monitor at a minimum such important areas as: \nbiogeochemical cycles, direct effects of biology on ocean physics, coastal resources, and fisheries sustainability. The oceans are an important net sink for carbon dioxide released by the burning of fossil fuels. However, because the great spatial and temporal variability of fluxes of carbon dioxide into and out of the ocean are poorly understood, the nature and sustainability of this critical process is insufficiently understood. It is known that the uptake of carbon dioxide is related directly to the abundance of marine algae, which can only be effectively monitored on a global scale through ocean colour. The relative abundance of certain types of marine algae, \nalso affect the ability of the oceans to absorb carbon dioxide by affecting the amount of calcium in the oceans, creating the potential for positive feedback between ocean warming and ocean biology. \nChanges in alga abundance and species composition affect the extent to which solar \nradiation is absorbed or reflected by the surface ocean. Such changes will alter local and global oceanic heat budgets, with implications for both local and global climate. Trace gases produced by marine algae and released from the ocean to the atmosphere can affect local climates directly through their effect on cloudiness. Half the world’s population lives within 100 kilometres of the ocean. This huge population has a large impact on the coastal zone. Rivers discharge large amounts of nutrients and sediments, much of it derived from human activities, into coastal waters, affecting water quality, recreational opportunities and coastal fisheries. Blooms of toxic algae affect human health both directly and indirectly, most notably through their effects on shellfish. Ocean colour will allow us to better detect, monitor, asses and mitigate the impacts of these events. \nOcean colour measurements will provide data to support the rational management of \nliving marine resources including aquiculture, Fish populations aggregate at areas of \ndiscontinuity between oceanic water masses; because phytoplankton growth may change across such boundaries, ocean colour gives the capability to map the surface manifestation of structure. \nUtilizing such information, scientists will be able to understand more fully how fish stocks respond to this structure. They will provide the tools that will give managers enhanced capability to intelligently manage and control these living marine resources. This will contribute to the efficient use and sustainability of these resources, \nThe deliverables of ocean colour remote sensing reflect the major science issues \nidentified in earlier sections. \nThese issues are: \n(i) the role of the oceans in climate change; \n(ii) the assessment of natural and anthropogenic impacts in the coastal zone and shelf seas; \n(iii) the monitoring and management of fisheries and the ecosystem of which the fish are part. \nThese deliverables can be categorized according to their utilization (unprioritized): \n(i) Science research products (time series, annual to decadel time scale) ; \n(ii) Operational products (short-term, daily to seasonal time scales); \n(iii) Methodological validation products. \nWithin the first category are data for ocean models which will enhance our ability to \nforecast global change. These global scale observations will lead to the definition of \nbiogeochemical premises and their characteristics which will serve as the basis for operational products. Operational products are required for environmental impact assessment, coastal zone management models, fisheries management and living resources protection. \nThe third category data is required to validate certain research approaches, and \nmethodologies used in assessing ocean colour. This category also includes inter-comparison of different ocean colour sensors to improve understanding of global scale processes and insure data set interoperability.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,159 | 0,133 |
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 source (Gemma direct ou Codex distillé), 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 ».