Additional file 1 of Unveiling the hidden economic toll of biological invasions in the European Union
Notice bibliographique
Résumé
Additional file 1: Description of the sectors considered in the InvaCost database. Additional file 2: Site-level costs recorded in InvaCost for the European Union member states (a), by type of cost (b) and impacted sector (c) with and without high-leverage points and the respective projected additional cost and percent increase. Additional file 3: Detailed breakdown of the considered models included in the model averaging. Table 1: Country-level analyses. Values of the fitted parameters for each predictor combination and model performance estimated using Akaike’s information criterion (AIC). We calculated Akaike’s weights for model averaging following Burnham & Anderson [57]. Table 2: Country-level analyses without high-leverage points. Values of the fitted parameters for each predictor combination and model performance estimated using Akaike’s information criterion (AIC). Model outputs for each combination of predictors. We calculated Akaike’s weights for model averaging following Burnham & Anderson [57]. Table 3: Site-level analyses (A) with all data and (B) without high-leverage points. Values of the fitted parameters for each predictor combination and model performance estimated using Akaike’s information criterion (AIC). Model outputs for each combination of predictors. We calculated Akaike’s weights for model averaging following Burnham & Anderson [57]. Additional file 4: Invasion costs (total costs and highly reliable, observed costs) for European Union member states recorded in InvaCost v4.1, in 2017 US$ billion. Additional file 5: Comparison of the number of established alien species in the European Union and alien species in InvaCost v4.1. Additional file 6. Established alien species of Union concern recorded among the established alien species in the European Union with the first European Union member state they were recorded in and the year of first record [48]. Additional file 7: Breakdown of interpolated costs excluding high-leverage points. Additional file 8: Country-level costs recorded in InvaCost with and without high-leverage points and the respective projected additional cost and percent increase. Additional file 9: Country-level costs recorded in InvaCost by type of cost (a) and impacted sector (b) with and without extreme values and the respective projected additional cost and percent increase. Additional file 10: Temporal projection of total annual management costs (a), recorded species per year with management costs (b), and number of reported references on management expenditure in InvaCost v4.1 per year (c) using linear (left, in blue) and quadratic (right, in orange) robust regressions and their respective confidence intervals. Solid dots and lines represent trends without the inclusion of extreme values. Open circles and dashed lines represent the respective trends when including all data (i.e., including extreme values). Grey dots present data for the period 1960–1979 that are not considered in the projection due to an absence of repeated measures of individual cost entries.
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 | 0,002 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,789 | 0,149 |
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 ».