CLiP South Africa Microplastics in the Port of Durban 2019
Notice bibliographique
Résumé
This dataset contains two csv files. The first one (South_Africa_Port_Durban_Microplastics.csv) reports the results of the study carried out in the Port of Durban in 2019. The file contains water measurement from CTD casts, microplastic abundance in water sampled by microplastic pump, microplastic abundance in sediment and particulate size analysis (PSA) results from Van Veen grab samples. GPS coordinates and time of deployment are reported for each measurement. CTD measured temperature, salinity and turbulence of the water. For microplastic pump casts, the amount of water filtered, the size of the four sieves used and the number of particles found on each filter are reported. Data from the grabs include PSA results and the number of particles found in the replicates (5g each) from each grab with lab blank values. A series of atmospheric blanks was also obtained leaving a jar open during sampling operation and microplastics abundances are reported. The csv second file (South_Africa_Port_Durban_Microplastics_FTIR) contains the profiles of the ATR-FT-IR spectrum analysis of plastic pieces found in the water samples. A .zip folder contains additional 18 FTIR profiles from water samples for which only a .tif image is available. A README text file contains the legend of the columns of the two csv files. The Commonwealth Litter Project (CLiP) supported South Africa to take action on plastics entering the oceans. The abundance of microplastics was investigated within The Port of Durban in the Durban Harbour (east coast of South Africa). A handheld CTD multi-channel logger (RBRconcerto3 C.T.D++, RBR Ltd., Canada), with attached optical backscatter turbidity (STM, Seapoint Sensors Inc, USA), was used to measure temperature, salinity and turbidity. Microplastics in water were sampled using a microplastic pump (KC Denmark Plankton Pump for Microplastics, Model 23.580) deployed through a crane from the quayside. The pump filtered 2000lt of water several sieves: 5mm, 500µm, 300µm, 200µm and 100µm . Sediment samples were collected at each microplastic pump site for sediment particle size analysis (PSA) and sediment microplastic analysis. Also, 15 additional sediment samples were taken from a boat on channels and areas of deposition. Five additional samples were taken in the harbour and surrounds where substrate was suitable. Each sediment and water sample was transferred to glass collecting pots. The sediment samples were dried at 50 degrees Celsius and, once dried, 5g triplicates were taken from a homogenised sample and underwent density separation before being chemically digested with a 30 percent KOH:NaClO solution. Each sample was then incubated for 72 hours before filtration Identification of the extracted microplastics was carried out using the fluorescence tagging of polymers using Nile Red coupled with digital imaging (Maes et al., 2017). For each sediment sample, a PSA was carried-out to relate abundance of microplastics to sediment type. Samples were freezed and then underwent PSA, based on a modified NMBAQC protocol from Mason (2011) for fast PSA screening based on wet splitting into silt/clay ( 63 µm), sand (63 µm – 4 mm) and gravel (> 4 mm) fractions. Once dry, samples were weighed, and the proportion of each fraction was calculated. Surface water samples were inspected for any suspected anthropogenic particles. Mesoplastics were manually removed, dried and characterised with ATR-FTIR. Samples with low to no organic content were filtered and stained with Nile Red before imaging and particle counting (Maes et al., 2017). For samples with high organic content, sieve rinse water was digested with a 30% KOH:NaClO solution with 24 hours incubation. Visible particles were analysed using ATR-FT-IR to identify polymer composition, comparing their spectrum to a polymers library. ATR-FTIR is the attenuated total reflection Fourier Transform infrared spectroscopy. A Thermo Fisher Scientific Nicolet iS5 ATR-FTIR with an OMNIC software (version 9.9.473) was used and polymers were identified based on the percentage match of IR spectra to a polymer library. Only spectra matched greater than 70 percent were accepted. Spectra were collected in the range 4000 – 650 1/cm at a resolution of 4 1/cm.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,046 | 0,028 |
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 ».